Ship validated WebGPU Q1 turbo runtime

Chrome exact-token A/B: raw Q1 vec4 +18.9% median on 1.7B; accepted 27B optimization sequence about +26% GPU throughput-equivalent. Final trace-free Chrome and physical Safari 26 gates passed on 1.7B and 27B with one graph split, zero CPU ops, and full WebGPU layer placement.

README.md

@@ -181,12 +181,14 @@ stay in the browser; only model/static asset downloads contact Hugging Face.
 - Dawn native WebGPU validation: `18eb229ef5f707c1464cc581252e7603c73a3ef0`
 - custom wllama source: `912c18b75d4358c1405a64646b8dbe43a205943b`
 - custom nested llama.cpp: `00fa7cb284cbf133fc426733bd64238a3588a33e`
-- custom patch set: `0a1d7ac5b0edb62cdb8ff5e5e0005c07b5a2fb7a91059f678c49d973fa456949`
-- custom ESM: `9a3d15ca29924927b6e95971c5b355f54b581f74089bd74975a063416f7fc65d`
-- JSPI WASM: `93ebb472969c4ccfe9d406e2ebd043da288a6d513a180a57827211278f4c82ed`
-- compat WASM: `91f1566e3dfa4cd591d902c03bfc2c92c9ceef8d5ce93cecae197ee1f4f5dd5d`
+- custom patch set: `60a5280bea5fc5ae11065f4c83fd2d532526cdcb7977cbe927cac7ae90926bde`
+- custom ESM: `a02f02b86d7dac8e3bd6c21225910c755bb85ec64ac7d323bfbfa2477b225e3d`
+- JSPI WASM: `dbded8334921b51877ade32009bcaee5b7423e3b39679089a970062c57128acb`
+- compat WASM: `f9b1bd2bd2a62a6b95ab52ecda92d10c709bc1ac3b6be9bb450e5adeff8dd39f`
 - compat worker: `0b667db536815fb9e5dc9ffbfbd6ba29affdda38a4ba892d5372ddb41ce9a8c8`
-- JSPI Q1_0 profile: raw-layout vec4 WG64; Safari compat Q1_0 profile: scalar
+- JSPI Q1_0 profile: raw-layout vec4 WG64 with shape-aware 4/8/16-output
+  tiling, direct GDN cache writes, and dual FFN projection + SwiGLU fusion;
+  Safari compat Q1_0 profile: scalar/conservative
 
 The vendored runtime exposes the validated token-embedding WebGPU placement
 needed for single-graph Bonsai inference and fails closed when placement or the

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dist/assets/graphql-aweuWxpE.js

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dist/assets/nginx-B19-5Nbj.js

@@ -0,0 +1 @@
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dist/assets/perl-CPcQPXT6.js

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as default};

dist/assets/xml-yX2vS4f7.js

@@ -0,0 +1 @@
+import{t as e}from"./java-Cf25h0JE.js";var t=Object.freeze(JSON.parse(`{"displayName":"XML","name":"xml","patterns":[{"begin":"(<\\\\?)\\\\s*([-0-9A-Z_a-z]+)","captures":{"1":{"name":"punctuation.definition.tag.xml"},"2":{"name":"entity.name.tag.xml"}},"end":"(\\\\?>)","name":"meta.tag.preprocessor.xml","patterns":[{"match":" ([-A-Za-z]+)","name":"entity.other.attribute-name.xml"},{"include":"#doublequotedString"},{"include":"#singlequotedString"}]},{"begin":"(<!)(DOCTYPE)\\\\s+([:A-Z_a-z][-.0-:A-Z_a-z]*)","captures":{"1":{"name":"punctuation.definition.tag.xml"},"2":{"name":"keyword.other.doctype.xml"},"3":{"name":"variable.language.documentroot.xml"}},"end":"\\\\s*(>)","name":"meta.tag.sgml.doctype.xml","patterns":[{"include":"#internalSubset"}]},{"include":"#comments"},{"begin":"(<)((?:([-0-9A-Z_a-z]+)(:))?([-0-:A-Z_a-z]+))(?=(\\\\s[^>]*)?></\\\\2>)","beginCaptures":{"1":{"name":"punctuation.definition.tag.xml"},"2":{"name":"entity.name.tag.xml"},"3":{"name":"entity.name.tag.namespace.xml"},"4":{"name":"punctuation.separator.namespace.xml"},"5":{"name":"entity.name.tag.localname.xml"}},"end":"(>)(</)((?:([-0-9A-Z_a-z]+)(:))?([-0-:A-Z_a-z]+))(>)","endCaptures":{"1":{"name":"punctuation.definition.tag.xml"},"2":{"name":"punctuation.definition.tag.xml"},"3":{"name":"entity.name.tag.xml"},"4":{"name":"entity.name.tag.namespace.xml"},"5":{"name":"punctuation.separator.namespace.xml"},"6":{"name":"entity.name.tag.localname.xml"},"7":{"name":"punctuation.definition.tag.xml"}},"name":"meta.tag.no-content.xml","patterns":[{"include":"#tagStuff"}]},{"begin":"(</?)(?:([-.\\\\w]+)((:)))?([-.:\\\\w]+)","captures":{"1":{"name":"punctuation.definition.tag.xml"},"2":{"name":"entity.name.tag.namespace.xml"},"3":{"name":"entity.name.tag.xml"},"4":{"name":"punctuation.separator.namespace.xml"},"5":{"name":"entity.name.tag.localname.xml"}},"end":"(/?>)","name":"meta.tag.xml","patterns":[{"include":"#tagStuff"}]},{"include":"#entity"},{"include":"#bare-ampersand"},{"begin":"<%@","beginCaptures":{"0":{"name":"punctuation.section.embedded.begin.xml"}},"end":"%>","endCaptures":{"0":{"name":"punctuation.section.embedded.end.xml"}},"name":"source.java-props.embedded.xml","patterns":[{"match":"page|include|taglib","name":"keyword.other.page-props.xml"}]},{"begin":"<%[!=]?(?!--)","beginCaptures":{"0":{"name":"punctuation.section.embedded.begin.xml"}},"end":"(?!--)%>","endCaptures":{"0":{"name":"punctuation.section.embedded.end.xml"}},"name":"source.java.embedded.xml","patterns":[{"include":"source.java"}]},{"begin":"<!\\\\[CDATA\\\\[","beginCaptures":{"0":{"name":"punctuation.definition.string.begin.xml"}},"end":"]]>","endCaptures":{"0":{"name":"punctuation.definition.string.end.xml"}},"name":"string.unquoted.cdata.xml"}],"repository":{"EntityDecl":{"begin":"(<!)(ENTITY)\\\\s+(%\\\\s+)?([:A-Z_a-z][-.0-:A-Z_a-z]*)(\\\\s+(?:SYSTEM|PUBLIC)\\\\s+)?","captures":{"1":{"name":"punctuation.definition.tag.xml"},"2":{"name":"keyword.other.entity.xml"},"3":{"name":"punctuation.definition.entity.xml"},"4":{"name":"variable.language.entity.xml"},"5":{"name":"keyword.other.entitytype.xml"}},"end":"(>)","patterns":[{"include":"#doublequotedString"},{"include":"#singlequotedString"}]},"bare-ampersand":{"match":"&","name":"invalid.illegal.bad-ampersand.xml"},"comments":{"patterns":[{"begin":"<%--","captures":{"0":{"name":"punctuation.definition.comment.xml"},"end":"--%>","name":"comment.block.xml"}},{"begin":"<!--","captures":{"0":{"name":"punctuation.definition.comment.xml"}},"end":"-->","name":"comment.block.xml","patterns":[{"begin":"--(?!>)","captures":{"0":{"name":"invalid.illegal.bad-comments-or-CDATA.xml"}}}]}]},"doublequotedString":{"begin":"\\"","beginCaptures":{"0":{"name":"punctuation.definition.string.begin.xml"}},"end":"\\"","endCaptures":{"0":{"name":"punctuation.definition.string.end.xml"}},"name":"string.quoted.double.xml","patterns":[{"include":"#entity"},{"include":"#bare-ampersand"}]},"entity":{"captures":{"1":{"name":"punctuation.definition.constant.xml"},"3":{"name":"punctuation.definition.constant.xml"}},"match":"(&)([:A-Z_a-z][-.0-:A-Z_a-z]*|#[0-9]+|#x\\\\h+)(;)","name":"constant.character.entity.xml"},"internalSubset":{"begin":"(\\\\[)","captures":{"1":{"name":"punctuation.definition.constant.xml"}},"end":"(])","name":"meta.internalsubset.xml","patterns":[{"include":"#EntityDecl"},{"include":"#parameterEntity"},{"include":"#comments"}]},"parameterEntity":{"captures":{"1":{"name":"punctuation.definition.constant.xml"},"3":{"name":"punctuation.definition.constant.xml"}},"match":"(%)([:A-Z_a-z][-.0-:A-Z_a-z]*)(;)","name":"constant.character.parameter-entity.xml"},"singlequotedString":{"begin":"'","beginCaptures":{"0":{"name":"punctuation.definition.string.begin.xml"}},"end":"'","endCaptures":{"0":{"name":"punctuation.definition.string.end.xml"}},"name":"string.quoted.single.xml","patterns":[{"include":"#entity"},{"include":"#bare-ampersand"}]},"tagStuff":{"patterns":[{"captures":{"1":{"name":"entity.other.attribute-name.namespace.xml"},"2":{"name":"entity.other.attribute-name.xml"},"3":{"name":"punctuation.separator.namespace.xml"},"4":{"name":"entity.other.attribute-name.localname.xml"}},"match":"(?:^|\\\\s+)(?:([-.\\\\w]+)((:)))?([-.:\\\\w]+)\\\\s*="},{"include":"#doublequotedString"},{"include":"#singlequotedString"}]}},"scopeName":"text.xml","embeddedLangs":["java"]}`)),n=[...e,t];export{n as default};

dist/index.html

@@ -10,7 +10,7 @@
       content="Bonsai Chat runs private WebGPU language models locally in your browser."
     />
     <title>Bonsai Chat · WebGPU</title>
-    <script type="module" crossorigin src="/assets/index-BG1J5OCa.js"></script>
+    <script type="module" crossorigin src="/assets/index-DjqZvGjf.js"></script>
     <link rel="stylesheet" crossorigin href="/assets/index-BE6Zfvfx.css">
   </head>
   <body>

dist/wasm/wllama-compat.wasm

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:91f1566e3dfa4cd591d902c03bfc2c92c9ceef8d5ce93cecae197ee1f4f5dd5d
-size 14872378
+oid sha256:f9b1bd2bd2a62a6b95ab52ecda92d10c709bc1ac3b6be9bb450e5adeff8dd39f
+size 14885188

dist/wasm/wllama.wasm

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:93ebb472969c4ccfe9d406e2ebd043da288a6d513a180a57827211278f4c82ed
-size 8026640
+oid sha256:dbded8334921b51877ade32009bcaee5b7423e3b39679089a970062c57128acb
+size 8048681

public/wasm/wllama-compat.wasm

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:91f1566e3dfa4cd591d902c03bfc2c92c9ceef8d5ce93cecae197ee1f4f5dd5d
-size 14872378
+oid sha256:f9b1bd2bd2a62a6b95ab52ecda92d10c709bc1ac3b6be9bb450e5adeff8dd39f
+size 14885188

public/wasm/wllama.wasm

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:93ebb472969c4ccfe9d406e2ebd043da288a6d513a180a57827211278f4c82ed
-size 8026640
+oid sha256:dbded8334921b51877ade32009bcaee5b7423e3b39679089a970062c57128acb
+size 8048681

src/bench/report.ts

@@ -170,6 +170,7 @@ export interface BenchmarkReport {
     opsOnCpu: number;
     gpuLayers: BackendReport['layersGpu'];
     confirmedBackends: string[];
+    webgpuTrace: string[];
   };
   runtime: {
     implementation: EngineCapabilities['runtime']['implementation'];
@@ -649,6 +650,7 @@ export function buildBenchmarkReport(observation: BenchmarkObservation): Benchma
       opsOnCpu: backendReport.opsOnCpu,
       gpuLayers: backendReport.layersGpu,
       confirmedBackends: [...backendReport.backends],
+      webgpuTrace: [...backendReport.webgpuTrace],
     },
     runtime: {
       implementation: capabilities.runtime.implementation,

vendor/wllama-bonsai/PATCH.diff

@@ -177,19 +177,30 @@ index 56b945c..14a4c88 100644
    "repository": {
      "type": "git",
 diff --git a/scripts/docker-compose.yml b/scripts/docker-compose.yml
-index 107f45a..7c8b7d5 100644
+index 107f45a..10b8813 100644
 --- a/scripts/docker-compose.yml
 +++ b/scripts/docker-compose.yml
-@@ -13,6 +13,8 @@ services:
+@@ -13,6 +13,19 @@ services:
      environment:
        - WLLAMA_TEST_BACKEND=${WLLAMA_TEST_BACKEND:-}
        - SKIP_COMPAT=${SKIP_COMPAT:-}
 +      - GGML_WEBGPU_Q1_VEC4=${GGML_WEBGPU_Q1_VEC4:-}
 +      - GGML_WEBGPU_Q1_VEC4_WG_SIZE=${GGML_WEBGPU_Q1_VEC4_WG_SIZE:-64}
++      - GGML_WEBGPU_Q1_VEC4_OUTPUTS_PER_WG=${GGML_WEBGPU_Q1_VEC4_OUTPUTS_PER_WG:-4}
++      - GGML_WEBGPU_Q1_VEC4_SHAPE_AWARE_OUTPUTS=${GGML_WEBGPU_Q1_VEC4_SHAPE_AWARE_OUTPUTS:-}
++      - GGML_WEBGPU_Q1_VEC4_LARGE_K_THRESHOLD=${GGML_WEBGPU_Q1_VEC4_LARGE_K_THRESHOLD:-4096}
++      - GGML_WEBGPU_Q1_ROW_RETILE=${GGML_WEBGPU_Q1_ROW_RETILE:-}
++      - GGML_WEBGPU_COMMAND_SUBMIT_BATCH_SIZE=${GGML_WEBGPU_COMMAND_SUBMIT_BATCH_SIZE:-64}
++      - GGML_WEBGPU_TURBO_TRACE=${GGML_WEBGPU_TURBO_TRACE:-}
++      - GGML_WEBGPU_GPU_PROFILE=${GGML_WEBGPU_GPU_PROFILE:-}
++      - GGML_WEBGPU_BATCH_PARAM_UPLOADS=${GGML_WEBGPU_BATCH_PARAM_UPLOADS:-}
++      - GGML_WEBGPU_Q1_GLU_FUSION=${GGML_WEBGPU_Q1_GLU_FUSION:-}
++      - GGML_WEBGPU_Q1_DUAL_GLU_FUSION=${GGML_WEBGPU_Q1_DUAL_GLU_FUSION:-}
++      - GGML_WEBGPU_GDN_CACHE_FUSION=${GGML_WEBGPU_GDN_CACHE_FUSION:-}
      volumes:
        - ..:/source:Z
      entrypoint: /bin/bash
-@@ -34,12 +36,20 @@ services:
+@@ -34,12 +47,65 @@ services:
            "https://github.com/google/dawn/releases/download/$${DAWN_TAG}/$${EMDAWN_PKG}"
          python3 -c "import zipfile; zf=zipfile.ZipFile('emdawn.zip','r'); zf.extractall('/source/build/emdawn'); zf.close()"
  
@@ -200,20 +211,65 @@ index 107f45a..7c8b7d5 100644
 +          TEST_BACKEND_FLAG="-DWLLAMA_TEST_BACKEND=ON";
          fi
 +        JSPI_Q1_VEC4="$${GGML_WEBGPU_Q1_VEC4:-1}"
++        case "$${GGML_WEBGPU_Q1_VEC4_SHAPE_AWARE_OUTPUTS:-0}" in
++          0|'') Q1_SHAPE_AWARE_FLAGS="-DGGML_WEBGPU_Q1_VEC4_SHAPE_AWARE_OUTPUTS=OFF" ;;
++          1) Q1_SHAPE_AWARE_FLAGS="-DGGML_WEBGPU_Q1_VEC4_SHAPE_AWARE_OUTPUTS=ON -DGGML_WEBGPU_Q1_VEC4_LARGE_K_THRESHOLD=$${GGML_WEBGPU_Q1_VEC4_LARGE_K_THRESHOLD}" ;;
++          *) echo "GGML_WEBGPU_Q1_VEC4_SHAPE_AWARE_OUTPUTS must be 0 or 1" >&2; exit 2 ;;
++        esac
++        case "$${GGML_WEBGPU_Q1_ROW_RETILE:-0}" in
++          0|'') Q1_ROW_RETILE_FLAG="-DGGML_WEBGPU_Q1_ROW_RETILE=OFF" ;;
++          1) Q1_ROW_RETILE_FLAG="-DGGML_WEBGPU_Q1_ROW_RETILE=ON" ;;
++          *) echo "GGML_WEBGPU_Q1_ROW_RETILE must be 0 or 1" >&2; exit 2 ;;
++        esac
 +        case "$${JSPI_Q1_VEC4}" in
 +          0) JSPI_Q1_FLAGS="-DGGML_WEBGPU_Q1_VEC4=OFF" ;;
-+          1) JSPI_Q1_FLAGS="-DGGML_WEBGPU_Q1_VEC4=ON -DGGML_WEBGPU_Q1_VEC4_WG_SIZE=$${GGML_WEBGPU_Q1_VEC4_WG_SIZE}" ;;
++          1) JSPI_Q1_FLAGS="-DGGML_WEBGPU_Q1_VEC4=ON -DGGML_WEBGPU_Q1_VEC4_WG_SIZE=$${GGML_WEBGPU_Q1_VEC4_WG_SIZE} -DGGML_WEBGPU_Q1_VEC4_OUTPUTS_PER_WG=$${GGML_WEBGPU_Q1_VEC4_OUTPUTS_PER_WG} $${Q1_SHAPE_AWARE_FLAGS} $${Q1_ROW_RETILE_FLAG}" ;;
 +          *) echo "GGML_WEBGPU_Q1_VEC4 must be 0 or 1" >&2; exit 2 ;;
 +        esac
-+        JSPI_CMAKE_EXTRA_FLAGS="$${TEST_BACKEND_FLAG} $${JSPI_Q1_FLAGS}"
-+        COMPAT_CMAKE_EXTRA_FLAGS="$${TEST_BACKEND_FLAG} -DGGML_WEBGPU_Q1_VEC4=OFF"
++        case "$${GGML_WEBGPU_COMMAND_SUBMIT_BATCH_SIZE:-64}" in
++          ''|*[!0-9]*) echo "GGML_WEBGPU_COMMAND_SUBMIT_BATCH_SIZE must be a positive integer" >&2; exit 2 ;;
++          0) echo "GGML_WEBGPU_COMMAND_SUBMIT_BATCH_SIZE must be a positive integer" >&2; exit 2 ;;
++        esac
++        case "$${GGML_WEBGPU_TURBO_TRACE:-0}" in
++          0|'') TURBO_TRACE_FLAG="-DGGML_WEBGPU_TURBO_TRACE=OFF" ;;
++          1) TURBO_TRACE_FLAG="-DGGML_WEBGPU_TURBO_TRACE=ON" ;;
++          *) echo "GGML_WEBGPU_TURBO_TRACE must be 0 or 1" >&2; exit 2 ;;
++        esac
++        case "$${GGML_WEBGPU_GPU_PROFILE:-0}" in
++          0|'') GPU_PROFILE_FLAG="-DGGML_WEBGPU_GPU_PROFILE=OFF" ;;
++          1) GPU_PROFILE_FLAG="-DGGML_WEBGPU_GPU_PROFILE=ON" ;;
++          *) echo "GGML_WEBGPU_GPU_PROFILE must be 0 or 1" >&2; exit 2 ;;
++        esac
++        case "$${GGML_WEBGPU_BATCH_PARAM_UPLOADS:-0}" in
++          0|'') BATCH_PARAM_UPLOADS_FLAG="-DGGML_WEBGPU_BATCH_PARAM_UPLOADS=OFF" ;;
++          1) BATCH_PARAM_UPLOADS_FLAG="-DGGML_WEBGPU_BATCH_PARAM_UPLOADS=ON" ;;
++          *) echo "GGML_WEBGPU_BATCH_PARAM_UPLOADS must be 0 or 1" >&2; exit 2 ;;
++        esac
++        case "$${GGML_WEBGPU_Q1_GLU_FUSION:-0}" in
++          0|'') Q1_GLU_FUSION_FLAG="-DGGML_WEBGPU_Q1_GLU_FUSION=OFF" ;;
++          1) Q1_GLU_FUSION_FLAG="-DGGML_WEBGPU_Q1_GLU_FUSION=ON" ;;
++          *) echo "GGML_WEBGPU_Q1_GLU_FUSION must be 0 or 1" >&2; exit 2 ;;
++        esac
++        case "$${GGML_WEBGPU_Q1_DUAL_GLU_FUSION:-0}" in
++          0|'') Q1_DUAL_GLU_FUSION_FLAG="-DGGML_WEBGPU_Q1_DUAL_GLU_FUSION=OFF" ;;
++          1) Q1_DUAL_GLU_FUSION_FLAG="-DGGML_WEBGPU_Q1_DUAL_GLU_FUSION=ON" ;;
++          *) echo "GGML_WEBGPU_Q1_DUAL_GLU_FUSION must be 0 or 1" >&2; exit 2 ;;
++        esac
++        case "$${GGML_WEBGPU_GDN_CACHE_FUSION:-0}" in
++          0|'') GDN_CACHE_FUSION_FLAG="-DGGML_WEBGPU_GDN_CACHE_FUSION=OFF" ;;
++          1) GDN_CACHE_FUSION_FLAG="-DGGML_WEBGPU_GDN_CACHE_FUSION=ON" ;;
++          *) echo "GGML_WEBGPU_GDN_CACHE_FUSION must be 0 or 1" >&2; exit 2 ;;
++        esac
++        COMMON_WEBGPU_FLAGS="-DGGML_WEBGPU_COMMAND_SUBMIT_BATCH_SIZE=$${GGML_WEBGPU_COMMAND_SUBMIT_BATCH_SIZE:-64} $${TURBO_TRACE_FLAG} $${GPU_PROFILE_FLAG}"
++        JSPI_CMAKE_EXTRA_FLAGS="$${TEST_BACKEND_FLAG} $${JSPI_Q1_FLAGS} $${Q1_GLU_FUSION_FLAG} $${Q1_DUAL_GLU_FUSION_FLAG} $${GDN_CACHE_FUSION_FLAG} $${BATCH_PARAM_UPLOADS_FLAG} $${COMMON_WEBGPU_FLAGS}"
++        COMPAT_CMAKE_EXTRA_FLAGS="$${TEST_BACKEND_FLAG} -DGGML_WEBGPU_Q1_VEC4=OFF -DGGML_WEBGPU_Q1_ROW_RETILE=OFF -DGGML_WEBGPU_Q1_GLU_FUSION=OFF -DGGML_WEBGPU_Q1_DUAL_GLU_FUSION=OFF -DGGML_WEBGPU_GDN_CACHE_FUSION=OFF -DGGML_WEBGPU_BATCH_PARAM_UPLOADS=OFF $${COMMON_WEBGPU_FLAGS}"
  
 -        emcmake cmake .. -DGGML_WEBGPU=ON -DGGML_WEBGPU_JSPI=ON -DEMDAWNWEBGPU_DIR="$${EMDAWNWEBGPU_DIR}" $${CMAKE_EXTRA_FLAGS}
 +        emcmake cmake .. -DGGML_WEBGPU=ON -DGGML_WEBGPU_JSPI=ON -DEMDAWNWEBGPU_DIR="$${EMDAWNWEBGPU_DIR}" $${JSPI_CMAKE_EXTRA_FLAGS}
          emmake make wllama -j
  
          # go back to root
-@@ -54,7 +64,7 @@ services:
+@@ -54,7 +120,7 @@ services:
          if [ -z "${SKIP_COMPAT}" ]; then
            mkdir -p build-compat
            cd build-compat
@@ -437,38 +493,99 @@ index be62030..9f5144a 100644
        const outputLen = new Uint32Array(
          getHeapU8().buffer,
 diff --git a/llama.cpp/ggml/src/ggml-webgpu/CMakeLists.txt b/llama.cpp/ggml/src/ggml-webgpu/CMakeLists.txt
-index 1503a1e..eb4a1ab 100644
+index 1503a1e..a4cd824 100644
 --- a/llama.cpp/ggml/src/ggml-webgpu/CMakeLists.txt
 +++ b/llama.cpp/ggml/src/ggml-webgpu/CMakeLists.txt
-@@ -2,6 +2,9 @@ cmake_minimum_required(VERSION 3.13)
+@@ -2,6 +2,26 @@ cmake_minimum_required(VERSION 3.13)
  
  find_package(Python3 REQUIRED)
  
 +option(GGML_WEBGPU_Q1_VEC4 "Use the benchmark-only vector-dot Q1_0 matvec kernel" OFF)
-+set(GGML_WEBGPU_Q1_VEC4_WG_SIZE "64" CACHE STRING "Q1_0 vector-dot workgroup size (64 or 128)")
++set(GGML_WEBGPU_Q1_VEC4_WG_SIZE "64" CACHE STRING "Q1_0 vector-dot workgroup size (32, 64, or 128)")
++set(GGML_WEBGPU_Q1_VEC4_OUTPUTS_PER_WG "4" CACHE STRING "Q1_0 vector-dot output rows computed by each workgroup (4, 8, or 16)")
++option(GGML_WEBGPU_Q1_VEC4_SHAPE_AWARE_OUTPUTS "Use the configured Q1_0 output tile for small-K matrices and 8 rows for large-K matrices" OFF)
++set(GGML_WEBGPU_Q1_VEC4_LARGE_K_THRESHOLD "4096" CACHE STRING "Largest K dimension that uses the small-K Q1_0 output tile")
++option(GGML_WEBGPU_Q1_ROW_RETILE "Retile pairs of Q1_0 blocks for aligned code-word loads on WebGPU" OFF)
++set(GGML_WEBGPU_COMMAND_SUBMIT_BATCH_SIZE "64" CACHE STRING "Maximum kernels encoded into one WebGPU command submission")
++option(GGML_WEBGPU_TURBO_TRACE "Log per-graph WebGPU encoding and submission counts" OFF)
++option(GGML_WEBGPU_BATCH_PARAM_UPLOADS "Batch per-dispatch uniforms into one upload per command submission" OFF)
++option(GGML_WEBGPU_Q1_GLU_FUSION "Fuse decode-only Q1_0 FFN second projection with its SwiGLU epilogue" OFF)
++option(GGML_WEBGPU_Q1_DUAL_GLU_FUSION "Fuse both decode-only Q1_0 FFN projections with SwiGLU" OFF)
++option(GGML_WEBGPU_GDN_CACHE_FUSION "Write Gated Delta Net state snapshots directly into the cache" OFF)
++
++if (NOT GGML_WEBGPU_COMMAND_SUBMIT_BATCH_SIZE MATCHES "^[1-9][0-9]*$")
++    message(FATAL_ERROR "GGML_WEBGPU_COMMAND_SUBMIT_BATCH_SIZE must be a positive integer")
++endif()
++if (GGML_WEBGPU_COMMAND_SUBMIT_BATCH_SIZE GREATER 16384)
++    message(FATAL_ERROR "GGML_WEBGPU_COMMAND_SUBMIT_BATCH_SIZE must not exceed 16384")
++endif()
 +
  # Shader locations
  set(SHADER_DIR "${CMAKE_CURRENT_SOURCE_DIR}/wgsl-shaders")
  set(SHADER_OUTPUT_DIR "${CMAKE_CURRENT_BINARY_DIR}/generated")
-@@ -39,6 +42,16 @@ ggml_add_backend_library(ggml-webgpu
+@@ -39,6 +59,60 @@ ggml_add_backend_library(ggml-webgpu
  
  add_dependencies(ggml-webgpu generate_shaders)
  
++target_compile_definitions(ggml-webgpu PRIVATE
++    GGML_WEBGPU_COMMAND_SUBMIT_BATCH_SIZE=${GGML_WEBGPU_COMMAND_SUBMIT_BATCH_SIZE}
++)
++
++if (GGML_WEBGPU_TURBO_TRACE)
++    target_compile_definitions(ggml-webgpu PRIVATE GGML_WEBGPU_TURBO_TRACE=1)
++endif()
++
++if (GGML_WEBGPU_BATCH_PARAM_UPLOADS)
++    target_compile_definitions(ggml-webgpu PRIVATE GGML_WEBGPU_BATCH_PARAM_UPLOADS=1)
++endif()
++
++if (GGML_WEBGPU_Q1_GLU_FUSION)
++    if (NOT GGML_WEBGPU_Q1_VEC4)
++        message(FATAL_ERROR "GGML_WEBGPU_Q1_GLU_FUSION requires GGML_WEBGPU_Q1_VEC4")
++    endif()
++    target_compile_definitions(ggml-webgpu PRIVATE GGML_WEBGPU_Q1_GLU_FUSION=1)
++endif()
++
++if (GGML_WEBGPU_Q1_DUAL_GLU_FUSION)
++    if (NOT GGML_WEBGPU_Q1_VEC4)
++        message(FATAL_ERROR "GGML_WEBGPU_Q1_DUAL_GLU_FUSION requires GGML_WEBGPU_Q1_VEC4")
++    endif()
++    target_compile_definitions(ggml-webgpu PRIVATE GGML_WEBGPU_Q1_DUAL_GLU_FUSION=1)
++endif()
++
++if (GGML_WEBGPU_GDN_CACHE_FUSION)
++    target_compile_definitions(ggml-webgpu PRIVATE GGML_WEBGPU_GDN_CACHE_FUSION=1)
++endif()
++
 +if (GGML_WEBGPU_Q1_VEC4)
-+    if (NOT GGML_WEBGPU_Q1_VEC4_WG_SIZE MATCHES "^(64|128)$")
-+        message(FATAL_ERROR "GGML_WEBGPU_Q1_VEC4_WG_SIZE must be 64 or 128")
++    if (NOT GGML_WEBGPU_Q1_VEC4_WG_SIZE MATCHES "^(32|64|128)$")
++        message(FATAL_ERROR "GGML_WEBGPU_Q1_VEC4_WG_SIZE must be 32, 64, or 128")
++    endif()
++    if (NOT GGML_WEBGPU_Q1_VEC4_OUTPUTS_PER_WG MATCHES "^(4|8|16)$")
++        message(FATAL_ERROR "GGML_WEBGPU_Q1_VEC4_OUTPUTS_PER_WG must be 4, 8, or 16")
++    endif()
++    if (NOT GGML_WEBGPU_Q1_VEC4_LARGE_K_THRESHOLD MATCHES "^[1-9][0-9]*$")
++        message(FATAL_ERROR "GGML_WEBGPU_Q1_VEC4_LARGE_K_THRESHOLD must be a positive integer")
 +    endif()
 +    target_compile_definitions(ggml-webgpu PRIVATE
 +        GGML_WEBGPU_Q1_VEC4=1
 +        GGML_WEBGPU_Q1_VEC4_WG_SIZE=${GGML_WEBGPU_Q1_VEC4_WG_SIZE}
++        GGML_WEBGPU_Q1_VEC4_OUTPUTS_PER_WG=${GGML_WEBGPU_Q1_VEC4_OUTPUTS_PER_WG}
++        GGML_WEBGPU_Q1_VEC4_LARGE_K_THRESHOLD=${GGML_WEBGPU_Q1_VEC4_LARGE_K_THRESHOLD}
 +    )
++    if (GGML_WEBGPU_Q1_VEC4_SHAPE_AWARE_OUTPUTS)
++        target_compile_definitions(ggml-webgpu PRIVATE GGML_WEBGPU_Q1_VEC4_SHAPE_AWARE_OUTPUTS=1)
++    endif()
++    if (GGML_WEBGPU_Q1_ROW_RETILE)
++        target_compile_definitions(ggml-webgpu PRIVATE GGML_WEBGPU_Q1_ROW_RETILE=1)
++    endif()
 +endif()
 +
  if(EMSCRIPTEN)
      set(EMDAWNWEBGPU_DIR "" CACHE PATH "Path to emdawnwebgpu_pkg")
  
 diff --git a/llama.cpp/ggml/src/ggml-webgpu/ggml-webgpu-shader-lib.hpp b/llama.cpp/ggml/src/ggml-webgpu/ggml-webgpu-shader-lib.hpp
-index d769236..3ebbd19 100644
+index d769236..26b178c 100644
 --- a/llama.cpp/ggml/src/ggml-webgpu/ggml-webgpu-shader-lib.hpp
 +++ b/llama.cpp/ggml/src/ggml-webgpu/ggml-webgpu-shader-lib.hpp
 @@ -50,6 +50,11 @@
@@ -483,7 +600,126 @@ index d769236..3ebbd19 100644
  
  #define WEBGPU_MUL_MAT_VEC_FLOAT_OUTPUTS_PER_WG    4
  #define WEBGPU_MUL_MAT_VEC_LEGACY_Q_OUTPUTS_PER_WG 4
-@@ -1931,7 +1936,15 @@ class ggml_webgpu_shader_lib {
+@@ -379,9 +384,10 @@ struct ggml_webgpu_gated_delta_net_pipeline_key {
+     int type;
+     int s_v;
+     int kda;
++    int fused_cache;
+ 
+     bool operator==(const ggml_webgpu_gated_delta_net_pipeline_key & other) const {
+-        return type == other.type && s_v == other.s_v && kda == other.kda;
++        return type == other.type && s_v == other.s_v && kda == other.kda && fused_cache == other.fused_cache;
+     }
+ };
+ 
+@@ -391,6 +397,7 @@ struct ggml_webgpu_gated_delta_net_pipeline_key_hash {
+         ggml_webgpu_hash_combine(seed, key.type);
+         ggml_webgpu_hash_combine(seed, key.s_v);
+         ggml_webgpu_hash_combine(seed, key.kda);
++        ggml_webgpu_hash_combine(seed, key.fused_cache);
+         return seed;
+     }
+ };
+@@ -907,10 +914,12 @@ struct ggml_webgpu_mul_mat_vec_pipeline_key {
+     int       vectorized;
+     uint32_t  num_cols;
+     bool      use_mmvq;
++    uint32_t  q1_outputs_per_wg;
+ 
+     bool operator==(const ggml_webgpu_mul_mat_vec_pipeline_key & other) const {
+         return src0_type == other.src0_type && src1_type == other.src1_type && vectorized == other.vectorized &&
+-               num_cols == other.num_cols && use_mmvq == other.use_mmvq;
++               num_cols == other.num_cols && use_mmvq == other.use_mmvq &&
++               q1_outputs_per_wg == other.q1_outputs_per_wg;
+     }
+ };
+ 
+@@ -922,6 +931,7 @@ struct ggml_webgpu_mul_mat_vec_pipeline_key_hash {
+         ggml_webgpu_hash_combine(seed, key.vectorized);
+         ggml_webgpu_hash_combine(seed, key.num_cols);
+         ggml_webgpu_hash_combine(seed, key.use_mmvq);
++        ggml_webgpu_hash_combine(seed, key.q1_outputs_per_wg);
+         return seed;
+     }
+ };
+@@ -1189,6 +1199,12 @@ class ggml_webgpu_shader_lib {
+         flash_attn_blk_pipelines;
+     std::unordered_map<ggml_webgpu_mul_mat_vec_pipeline_key, webgpu_pipeline, ggml_webgpu_mul_mat_vec_pipeline_key_hash>
+         mul_mat_vec_pipelines;   // fast mat-vec (n==1)
++#ifdef GGML_WEBGPU_Q1_GLU_FUSION
++    std::unordered_map<int, webgpu_pipeline> mul_mat_vec_q1_glu_pipelines;  // key is fixed
++#endif
++#ifdef GGML_WEBGPU_Q1_DUAL_GLU_FUSION
++    std::unordered_map<uint32_t, webgpu_pipeline> mul_mat_vec_q1_dual_glu_pipelines;  // outputs per workgroup
++#endif
+     std::unordered_map<ggml_webgpu_mul_mat_pipeline_key, webgpu_pipeline, ggml_webgpu_mul_mat_pipeline_key_hash>
+         mul_mat_fast_pipelines;  // fast mat-mat (reg-tile or subgroup)
+     std::unordered_map<ggml_webgpu_quantize_q8_pipeline_key, webgpu_pipeline, ggml_webgpu_quantize_q8_pipeline_key_hash>
+@@ -1591,6 +1607,11 @@ class ggml_webgpu_shader_lib {
+ 
+                     if (key.src_type == GGML_TYPE_Q1_0) {
+                         defines.push_back("BLOCK_SIZE=128u");
++#if defined(GGML_WEBGPU_Q1_ROW_RETILE)
++                        defines.push_back("Q1_ROW_RETILED");
++                        variant += "_row_retiled";
++#endif
++
+                     } else if ((key.src_type >= GGML_TYPE_Q4_0 && key.src_type <= GGML_TYPE_Q8_1) ||
+                                key.src_type == GGML_TYPE_IQ4_NL || key.src_type == GGML_TYPE_MXFP4) {
+                         defines.push_back("BLOCK_SIZE=32u");
+@@ -1786,11 +1807,13 @@ class ggml_webgpu_shader_lib {
+         return ssm_scan_pipelines[key];
+     }
+ 
+-    webgpu_pipeline get_gated_delta_net_pipeline(const ggml_webgpu_shader_lib_context & context) {
++    webgpu_pipeline get_gated_delta_net_pipeline(const ggml_webgpu_shader_lib_context & context,
++                                                  bool fused_cache = false) {
+         ggml_webgpu_gated_delta_net_pipeline_key key = {};
+         key.type                                     = context.dst->type;
+         key.s_v                                      = (int) context.src2->ne[0];
+         key.kda                                      = context.src3->ne[0] == context.src2->ne[0];
++        key.fused_cache                              = fused_cache;
+ 
+         auto it = gated_delta_net_pipelines.find(key);
+         if (it != gated_delta_net_pipelines.end()) {
+@@ -1813,6 +1836,11 @@ class ggml_webgpu_shader_lib {
+             variant += "_kda";
+         }
+ 
++        if (key.fused_cache) {
++            defines.push_back("FUSED_CACHE");
++            variant += "_cache";
++        }
++
+         defines.push_back("S_V=" + std::to_string(key.s_v) + "u");
+         defines.push_back("WG_SIZE=" + std::to_string(key.s_v) + "u");
+ 
+@@ -1899,6 +1927,24 @@ class ggml_webgpu_shader_lib {
+         key.num_cols   = context.dst->ne[1];
+         key.use_mmvq =
+             ggml_webgpu_can_use_mmvq(context.src0, context.src1, context.supports_dot_product, context.vendor);
++#if defined(GGML_WEBGPU_Q1_VEC4)
++        if (context.src0->type == GGML_TYPE_Q1_0) {
++#    if defined(GGML_WEBGPU_Q1_VEC4_SHAPE_AWARE_OUTPUTS)
++            const uint32_t q1_k = context.src0->ne[0];
++            const uint32_t q1_m = context.dst->ne[0];
++            if (q1_k <= GGML_WEBGPU_Q1_VEC4_LARGE_K_THRESHOLD ||
++                q1_m < GGML_WEBGPU_Q1_VEC4_LARGE_K_THRESHOLD) {
++                key.q1_outputs_per_wg = GGML_WEBGPU_Q1_VEC4_OUTPUTS_PER_WG;
++            } else if (q1_m >= GGML_WEBGPU_Q1_VEC4_LARGE_K_THRESHOLD && q1_m < q1_k) {
++                key.q1_outputs_per_wg = 16u;
++            } else {
++                key.q1_outputs_per_wg = 8u;
++            }
++#    else
++            key.q1_outputs_per_wg = GGML_WEBGPU_Q1_VEC4_OUTPUTS_PER_WG;
++#    endif
++        }
++#endif
+ 
+         auto it = mul_mat_vec_pipelines.find(key);
+         if (it != mul_mat_vec_pipelines.end()) {
+@@ -1931,7 +1977,15 @@ class ggml_webgpu_shader_lib {
                      std::transform(type_upper.begin(), type_upper.end(), type_upper.begin(), ::toupper);
  
                      defines.push_back("BYTE_HELPERS");
@@ -500,13 +736,29 @@ index d769236..3ebbd19 100644
                      defines.push_back("U32_DEQUANT_HELPERS");
                      defines.push_back("SRC0_INNER_TYPE=u32");
                      switch (context.src0->type) {
-@@ -1995,6 +2008,15 @@ class ggml_webgpu_shader_lib {
+@@ -1973,6 +2027,13 @@ class ggml_webgpu_shader_lib {
+                 }
+         }
+ 
++#if defined(GGML_WEBGPU_Q1_ROW_RETILE)
++        if (context.src0->type == GGML_TYPE_Q1_0) {
++            defines.push_back("Q1_ROW_RETILED");
++            variant += "_row_retiled";
++        }
++#endif
++
+         // src1 type (vector)
+         switch (context.src1->type) {
+             case GGML_TYPE_F32:
+@@ -1995,6 +2056,17 @@ class ggml_webgpu_shader_lib {
  
          if (key.src0_type == GGML_TYPE_Q1_0) {
              outputs_per_wg = WEBGPU_MUL_MAT_VEC_LEGACY_Q_OUTPUTS_PER_WG;
 +#if defined(GGML_WEBGPU_Q1_VEC4)
 +            wg_size = GGML_WEBGPU_Q1_VEC4_WG_SIZE;
++            outputs_per_wg = key.q1_outputs_per_wg;
 +            variant += "_wg" + std::to_string(wg_size);
++            variant += "_out" + std::to_string(outputs_per_wg);
 +#else
 +            if (!context.supports_subgroups) {
 +                wg_size = WEBGPU_MUL_MAT_VEC_Q1_NO_SG_WG_SIZE;
@@ -516,11 +768,1054 @@ index d769236..3ebbd19 100644
          } else if (key.src0_type >= GGML_TYPE_Q2_K) {
              outputs_per_wg = WEBGPU_MUL_MAT_VEC_K_Q_OUTPUTS_PER_WG;
          } else if (key.src0_type >= GGML_TYPE_Q4_0) {
+@@ -2008,7 +2080,12 @@ class ggml_webgpu_shader_lib {
+ 
+         defines.push_back(std::string("WG_SIZE=") + std::to_string(wg_size));
+         defines.push_back(std::string("OUTPUTS_PER_WG=") + std::to_string(outputs_per_wg));
+-        defines.push_back(context.supports_subgroups ? "USE_SUBGROUP_REDUCTION" : "USE_WORKGROUP_REDUCTION");
++        if (key.src0_type == GGML_TYPE_Q1_0 && wg_size == 32 && context.supports_subgroups) {
++            defines.push_back("USE_SUBGROUP_REDUCTION");
++            defines.push_back("USE_SINGLE_SUBGROUP_REDUCTION");
++        } else {
++            defines.push_back(context.supports_subgroups ? "USE_SUBGROUP_REDUCTION" : "USE_WORKGROUP_REDUCTION");
++        }
+         variant += context.supports_subgroups ? "_sg_reduce" : "_wg_reduce";
+         if (key.vectorized) {
+             variant += "_vectorized";
+@@ -2027,6 +2104,87 @@ class ggml_webgpu_shader_lib {
+         return mul_mat_vec_pipelines[key];
+     }
+ 
++#ifdef GGML_WEBGPU_Q1_GLU_FUSION
++    webgpu_pipeline get_mul_mat_vec_q1_glu_pipeline(const ggml_webgpu_shader_lib_context & context) {
++        GGML_ASSERT(context.src0->type == GGML_TYPE_Q1_0);
++        GGML_ASSERT(context.src1->type == GGML_TYPE_F32);
++        GGML_ASSERT(context.src2->type == GGML_TYPE_F32);
++        GGML_ASSERT(context.dst->type == GGML_TYPE_F32);
++        GGML_ASSERT(context.supports_subgroups);
++
++        auto it = mul_mat_vec_q1_glu_pipelines.find(1);
++        if (it != mul_mat_vec_q1_glu_pipelines.end()) {
++            return it->second;
++        }
++
++        const uint32_t         wg_size        = GGML_WEBGPU_Q1_VEC4_WG_SIZE;
++        const uint32_t         outputs_per_wg = WEBGPU_MUL_MAT_VEC_LEGACY_Q_OUTPUTS_PER_WG;
++        std::vector<std::string> defines = {
++            std::string("WG_SIZE=") + std::to_string(wg_size),
++            std::string("OUTPUTS_PER_WG=") + std::to_string(outputs_per_wg),
++        };
++        std::string variant = "mul_mat_vec_q1_glu_wg" + std::to_string(wg_size);
++#if defined(GGML_WEBGPU_Q1_ROW_RETILE)
++        defines.push_back("Q1_ROW_RETILED");
++        variant += "_row_retiled";
++#endif
++        auto processed = preprocessor.preprocess(wgsl_mul_mat_vec_q1_glu, defines);
++        auto decisions = std::make_shared<ggml_webgpu_mul_mat_vec_shader_decisions>();
++        decisions->wg_size        = wg_size;
++        decisions->outputs_per_wg = outputs_per_wg;
++        decisions->vec_size       = 1;
++
++        webgpu_pipeline pipeline = ggml_webgpu_create_pipeline(device, processed, variant);
++        pipeline.context = decisions;
++        mul_mat_vec_q1_glu_pipelines[1] = pipeline;
++        return mul_mat_vec_q1_glu_pipelines[1];
++    }
++#endif
++
++#ifdef GGML_WEBGPU_Q1_DUAL_GLU_FUSION
++    webgpu_pipeline get_mul_mat_vec_q1_dual_glu_pipeline(const ggml_webgpu_shader_lib_context & context) {
++        GGML_ASSERT(context.src0->type == GGML_TYPE_Q1_0);
++        GGML_ASSERT(context.src1->type == GGML_TYPE_Q1_0);
++        GGML_ASSERT(context.src2->type == GGML_TYPE_F32);
++        GGML_ASSERT(context.dst->type == GGML_TYPE_F32);
++        GGML_ASSERT(context.supports_subgroups);
++
++        const uint32_t q1_k = context.src0->ne[0];
++        const uint32_t q1_m = context.dst->ne[0];
++        uint32_t       outputs_per_wg;
++        if (q1_k <= 4096 || q1_m < 4096) {
++            outputs_per_wg = 4;
++        } else if (q1_m >= 4096 && q1_m < q1_k) {
++            outputs_per_wg = 16;
++        } else {
++            outputs_per_wg = 8;
++        }
++
++        auto it = mul_mat_vec_q1_dual_glu_pipelines.find(outputs_per_wg);
++        if (it != mul_mat_vec_q1_dual_glu_pipelines.end()) {
++            return it->second;
++        }
++
++        const uint32_t           wg_size = GGML_WEBGPU_Q1_VEC4_WG_SIZE;
++        std::vector<std::string> defines = {
++            std::string("WG_SIZE=") + std::to_string(wg_size),
++            std::string("OUTPUTS_PER_WG=") + std::to_string(outputs_per_wg),
++        };
++        std::string variant = "mul_mat_vec_q1_dual_glu_wg" + std::to_string(wg_size) + "_out" +
++                              std::to_string(outputs_per_wg);
++        auto processed = preprocessor.preprocess(wgsl_mul_mat_vec_q1_dual_glu, defines);
++        auto decisions = std::make_shared<ggml_webgpu_mul_mat_vec_shader_decisions>();
++        decisions->wg_size        = wg_size;
++        decisions->outputs_per_wg = outputs_per_wg;
++        decisions->vec_size       = 1;
++
++        webgpu_pipeline pipeline = ggml_webgpu_create_pipeline(device, processed, variant);
++        pipeline.context = decisions;
++        mul_mat_vec_q1_dual_glu_pipelines[outputs_per_wg] = pipeline;
++        return mul_mat_vec_q1_dual_glu_pipelines[outputs_per_wg];
++    }
++#endif
++
+     webgpu_pipeline get_mul_mat_fast_pipeline(const ggml_webgpu_shader_lib_context & context) {
+         ggml_webgpu_mul_mat_pipeline_key key = {};
+         key.src0_type                        = context.src0->type;
+@@ -2091,6 +2249,13 @@ class ggml_webgpu_shader_lib {
+                     defines.push_back("U32_DEQUANT_HELPERS");
+                     defines.push_back("SRC0_INNER_TYPE=u32");
+ 
++#if defined(GGML_WEBGPU_Q1_ROW_RETILE)
++                    if (context.src0->type == GGML_TYPE_Q1_0) {
++                        defines.push_back("Q1_ROW_RETILED");
++                        variant += "_row_retiled";
++                    }
++#endif
++
+                     switch (context.src0->type) {
+                         case GGML_TYPE_IQ1_S:
+                         case GGML_TYPE_IQ1_M:
+@@ -2263,6 +2428,13 @@ class ggml_webgpu_shader_lib {
+                     defines.push_back("U32_DEQUANT_HELPERS");
+                     defines.push_back("SRC0_INNER_TYPE=u32");
+ 
++#if defined(GGML_WEBGPU_Q1_ROW_RETILE)
++                    if (context.src0->type == GGML_TYPE_Q1_0) {
++                        defines.push_back("Q1_ROW_RETILED");
++                        variant += "_row_retiled";
++                    }
++#endif
++
+                     switch (context.src0->type) {
+                         case GGML_TYPE_IQ1_S:
+                         case GGML_TYPE_IQ1_M:
+diff --git a/llama.cpp/ggml/src/ggml-webgpu/ggml-webgpu.cpp b/llama.cpp/ggml/src/ggml-webgpu/ggml-webgpu.cpp
+index 29025e9..00a4920 100644
+--- a/llama.cpp/ggml/src/ggml-webgpu/ggml-webgpu.cpp
++++ b/llama.cpp/ggml/src/ggml-webgpu/ggml-webgpu.cpp
+@@ -17,12 +17,19 @@
+ #include <webgpu/webgpu_cpp.h>
+ 
+ #include <atomic>
++#if defined(GGML_WEBGPU_GPU_PROFILE) || defined(GGML_WEBGPU_GDN_CACHE_FUSION)
++#    include <algorithm>
++#endif
++#ifdef GGML_WEBGPU_TURBO_TRACE
++#    include <chrono>
++#endif
+ #include <cstdint>
+ #include <cstring>
+ #ifdef GGML_WEBGPU_GPU_PROFILE
+ #    include <iomanip>
+ #endif
+-#if defined(GGML_WEBGPU_DEBUG) || defined(GGML_WEBGPU_CPU_PROFILE) || defined(GGML_WEBGPU_GPU_PROFILE)
++#if defined(GGML_WEBGPU_DEBUG) || defined(GGML_WEBGPU_CPU_PROFILE) || defined(GGML_WEBGPU_GPU_PROFILE) || \
++    defined(GGML_WEBGPU_TURBO_TRACE)
+ #    include <iostream>
+ #endif
+ #include <memory>
+@@ -86,7 +93,11 @@ static inline uint32_t ggml_webgpu_u32_from_f32(float value) {
+ 
+ /* Constants */
+ 
+-#define WEBGPU_DEFAULT_COMMAND_SUBMIT_BATCH_SIZE 64u
++#ifndef GGML_WEBGPU_COMMAND_SUBMIT_BATCH_SIZE
++#    define GGML_WEBGPU_COMMAND_SUBMIT_BATCH_SIZE 64
++#endif
++
++#define WEBGPU_DEFAULT_COMMAND_SUBMIT_BATCH_SIZE (GGML_WEBGPU_COMMAND_SUBMIT_BATCH_SIZE + 0u)
+ #define WEBGPU_NUM_PARAM_SLOT_SAFETY_MARGIN      10u
+ #define WEBGPU_RUNTIME_WAIT_TIMEOUT_MS           30000u
+ #define WEBGPU_RUNTIME_WAIT_TIMEOUT_NS           (WEBGPU_RUNTIME_WAIT_TIMEOUT_MS * 1e6)
+@@ -119,8 +130,14 @@ static void ggml_webgpu_create_buffer(wgpu::Device &    device,
+ // cursor is reset immediately after that batch is submitted.
+ struct webgpu_param_arena {
+     wgpu::Buffer buffer;
++#ifdef GGML_WEBGPU_BATCH_PARAM_UPLOADS
++    std::vector<uint8_t> host_data;
++#endif
+     size_t       slot_stride = 0;
+     size_t       slot_size   = 0;
++#ifdef GGML_WEBGPU_BATCH_PARAM_UPLOADS
++    size_t       dirty_end   = 0;
++#endif
+     uint32_t     slot_count  = 0;
+     uint32_t     next_slot   = 0;
+ 
+@@ -129,6 +146,10 @@ struct webgpu_param_arena {
+         this->slot_size   = slot_size;
+         this->slot_count  = slot_count;
+         this->next_slot   = 0;
++#ifdef GGML_WEBGPU_BATCH_PARAM_UPLOADS
++        this->dirty_end   = 0;
++        this->host_data.resize(this->slot_stride * slot_count);
++#endif
+ 
+         ggml_webgpu_create_buffer(device, buffer, this->slot_stride * slot_count,
+                                   wgpu::BufferUsage::CopyDst | wgpu::BufferUsage::Uniform, "ggml_webgpu_param_arena");
+@@ -143,13 +164,39 @@ struct webgpu_param_arena {
+         return slot_stride * next_slot++;
+     }
+ 
+-    void reset() { next_slot = 0; }
++#ifdef GGML_WEBGPU_BATCH_PARAM_UPLOADS
++    void write_slot(size_t offset, const void * data, size_t size) {
++        GGML_ASSERT(offset + size <= host_data.size());
++        memcpy(host_data.data() + offset, data, size);
++        dirty_end = std::max(dirty_end, offset + size);
++    }
++
++    bool flush(wgpu::Queue & queue) {
++        if (dirty_end == 0) {
++            return false;
++        }
++        queue.WriteBuffer(buffer, 0, host_data.data(), dirty_end);
++        dirty_end = 0;
++        return true;
++    }
++#endif
++
++    void reset() {
++#ifdef GGML_WEBGPU_BATCH_PARAM_UPLOADS
++        GGML_ASSERT(dirty_end == 0);
++#endif
++        next_slot = 0;
++    }
+ 
+     void cleanup() {
+         if (buffer) {
+             buffer.Destroy();
+             buffer = nullptr;
+         }
++#ifdef GGML_WEBGPU_BATCH_PARAM_UPLOADS
++        host_data.clear();
++        dirty_end = 0;
++#endif
+     }
+ 
+     ~webgpu_param_arena() { this->cleanup(); }
+@@ -167,6 +214,9 @@ struct webgpu_dispatch_desc {
+     std::vector<uint32_t>             params;
+     std::vector<wgpu::BindGroupEntry> bind_group_entries;
+     std::pair<uint32_t, uint32_t>     workgroups = { 1, 1 };
++#ifdef GGML_WEBGPU_GPU_PROFILE
++    std::string profile_name = {};
++#endif
+ };
+ 
+ struct webgpu_capabilities {
+@@ -256,15 +306,21 @@ struct webgpu_context_struct {
+     wgpu::ComputePassEncoder active_compute_pass;
+     bool                     batch_compute_passes = true;
+ 
++#ifdef GGML_WEBGPU_TURBO_TRACE
++    uint64_t turbo_trace_graph_count = 0;
++#endif
++
+     size_t memset_bytes_per_thread;
+ 
+ #ifdef GGML_WEBGPU_GPU_PROFILE
+     // Profiling: per-shader GPU time in ms
+     std::unordered_map<std::string, double> shader_gpu_time_ms;
++    std::unordered_map<std::string, double> profile_graph_gpu_time_ms;
+     wgpu::Buffer                            profile_timestamp_dev_buf;
+     wgpu::Buffer                            profile_timestamp_host_buf;
+     wgpu::QuerySet                          profile_timestamp_query_set;
+     uint32_t                                profile_timestamp_query_count = 0;
++    uint64_t                                profile_graph_count = 0;
+ #endif
+ 
+     ~webgpu_context_struct() {
+@@ -536,7 +592,9 @@ static webgpu_encoded_op ggml_backend_webgpu_build_multi(webgpu_context &
+                                                          const std::vector<webgpu_dispatch_desc> & dispatches) {
+     webgpu_encoded_op            result = {};
+     std::vector<wgpu::BindGroup> bind_groups;
++#ifndef GGML_WEBGPU_BATCH_PARAM_UPLOADS
+     std::vector<size_t>          param_offsets;
++#endif
+     result.num_kernels = dispatches.size();
+ 
+     for (size_t i = 0; i < dispatches.size(); i++) {
+@@ -555,13 +613,19 @@ static webgpu_encoded_op ggml_backend_webgpu_build_multi(webgpu_context &
+         bind_group_desc.entries    = entries.data();
+         bind_group_desc.label      = dispatch.pipeline.name.c_str();
+         bind_groups.push_back(ctx->global_ctx->device.CreateBindGroup(&bind_group_desc));
++#ifdef GGML_WEBGPU_BATCH_PARAM_UPLOADS
++        ctx->param_arena.write_slot(param_offset, dispatch.params.data(), param_size);
++#else
+         param_offsets.push_back(param_offset);
++#endif
+     }
+ 
++#ifndef GGML_WEBGPU_BATCH_PARAM_UPLOADS
+     for (size_t i = 0; i < param_offsets.size(); i++) {
+         ctx->global_ctx->queue.WriteBuffer(ctx->param_arena.buffer, param_offsets[i], dispatches[i].params.data(),
+                                            dispatches[i].params.size() * sizeof(uint32_t));
+     }
++#endif
+ 
+ #ifdef GGML_WEBGPU_GPU_PROFILE
+     for (size_t i = 0; i < dispatches.size(); i++) {
+@@ -582,7 +646,8 @@ static webgpu_encoded_op ggml_backend_webgpu_build_multi(webgpu_context &
+         pass.SetBindGroup(0, bind_groups[i]);
+         pass.DispatchWorkgroups(dispatches[i].workgroups.first, dispatches[i].workgroups.second, 1);
+         pass.End();
+-        result.pipeline_names.push_back(dispatches[i].pipeline.name);
++        result.pipeline_names.push_back(dispatches[i].profile_name.empty() ? dispatches[i].pipeline.name :
++                                                                          dispatches[i].profile_name);
+     }
+ #else
+     for (size_t i = 0; i < dispatches.size(); i++) {
+@@ -1229,7 +1294,9 @@ static webgpu_encoded_op ggml_webgpu_gated_delta_net(webgpu_context & ctx,
+                                                      ggml_tensor *    src3,
+                                                      ggml_tensor *    src4,
+                                                      ggml_tensor *    src5,
+-                                                     ggml_tensor *    dst) {
++                                                     ggml_tensor *    dst,
++                                                     ggml_tensor *    state_cache = nullptr,
++                                                     uint32_t         cache_slot_stride = 0) {
+     ggml_webgpu_shader_lib_context shader_lib_ctx = {};
+     shader_lib_ctx.src0                           = src0;
+     shader_lib_ctx.src1                           = src1;
+@@ -1239,7 +1306,7 @@ static webgpu_encoded_op ggml_webgpu_gated_delta_net(webgpu_context & ctx,
+     shader_lib_ctx.dst                            = dst;
+     shader_lib_ctx.max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup;
+ 
+-    webgpu_pipeline pipeline = ctx->shader_lib->get_gated_delta_net_pipeline(shader_lib_ctx);
++    webgpu_pipeline pipeline = ctx->shader_lib->get_gated_delta_net_pipeline(shader_lib_ctx, state_cache != nullptr);
+ 
+     const uint32_t s_v      = (uint32_t) src2->ne[0];
+     const uint32_t h        = (uint32_t) src2->ne[1];
+@@ -1272,6 +1339,10 @@ static webgpu_encoded_op ggml_webgpu_gated_delta_net(webgpu_context & ctx,
+         (uint32_t) (src2->ne[3] / src0->ne[3]),
+         K,
+         scale_u32,
++        state_cache != nullptr
++            ? (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, state_cache) / ggml_type_size(state_cache->type))
++            : 0,
++        cache_slot_stride,
+     };
+ 
+     std::vector<wgpu::BindGroupEntry> entries = {
+@@ -1280,10 +1351,89 @@ static webgpu_encoded_op ggml_webgpu_gated_delta_net(webgpu_context & ctx,
+         ggml_webgpu_make_tensor_bind_group_entry(ctx, 4, src4), ggml_webgpu_make_tensor_bind_group_entry(ctx, 5, src5),
+         ggml_webgpu_make_tensor_bind_group_entry(ctx, 6, dst),
+     };
++    if (state_cache != nullptr) {
++        entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 7, state_cache));
++    }
+ 
+     return ggml_backend_webgpu_build(ctx, pipeline, params, entries, h, n_seqs);
+ }
+ 
++#ifdef GGML_WEBGPU_GDN_CACHE_FUSION
++struct ggml_webgpu_gdn_cache_fusion {
++    ggml_tensor * cache;
++    uint32_t      slot_stride;
++    int           num_encoded_ops;
++};
++
++static bool ggml_webgpu_is_view_or_noop(const ggml_tensor * tensor) {
++    return ggml_is_empty(tensor) || tensor->op == GGML_OP_RESHAPE || tensor->op == GGML_OP_TRANSPOSE ||
++           tensor->op == GGML_OP_VIEW || tensor->op == GGML_OP_PERMUTE || tensor->op == GGML_OP_NONE;
++}
++
++static std::optional<ggml_webgpu_gdn_cache_fusion> ggml_webgpu_try_gdn_cache_fusion(webgpu_context & ctx,
++                                                                                    ggml_cgraph *    cgraph,
++                                                                                    int node_idx) {
++    ggml_tensor * gdn = cgraph->nodes[node_idx];
++    if (gdn->op != GGML_OP_GATED_DELTA_NET || gdn->type != GGML_TYPE_F32 ||
++        (gdn->flags & GGML_TENSOR_FLAG_OUTPUT)) {
++        return std::nullopt;
++    }
++
++    const ggml_tensor * src_v = gdn->src[2];
++    if (src_v == nullptr) {
++        return std::nullopt;
++    }
++    const int64_t       s_v       = src_v->ne[0];
++    const int64_t       h         = src_v->ne[1];
++    const int64_t       n_tokens  = src_v->ne[2];
++    const int64_t       n_seqs    = src_v->ne[3];
++    const int64_t       d         = s_v * s_v * h;
++    const int64_t       k         = ggml_get_op_params_i32(gdn, 0);
++    if (k < 1 || n_tokens < 1) {
++        return std::nullopt;
++    }
++    const int64_t n_written = std::min<int64_t>(n_tokens, k);
++    const size_t  tail_off  = ggml_row_size(GGML_TYPE_F32, s_v * h * n_tokens * n_seqs);
++
++    ggml_tensor * cpy = nullptr;
++    int           cpy_idx = -1;
++    for (int j = node_idx + 1; j < cgraph->n_nodes; ++j) {
++        ggml_tensor * candidate = cgraph->nodes[j];
++        if (ggml_webgpu_is_view_or_noop(candidate)) {
++            continue;
++        }
++        if (candidate->op != GGML_OP_CPY || (candidate->flags & GGML_TENSOR_FLAG_OUTPUT)) {
++            return std::nullopt;
++        }
++        cpy     = candidate;
++        cpy_idx = j;
++        break;
++    }
++    if (cpy == nullptr) {
++        return std::nullopt;
++    }
++
++    ggml_tensor * src = cpy->src[0];
++    ggml_tensor * dst = cpy->src[1];
++    if (src == nullptr || dst == nullptr || src->op != GGML_OP_VIEW || src->view_src != gdn ||
++        src->view_offs != tail_off || !ggml_is_contiguous(src) || dst->op != GGML_OP_VIEW ||
++        dst->type != GGML_TYPE_F32 || dst->buffer == nullptr || dst->ne[0] != d || dst->ne[1] != n_seqs ||
++        dst->ne[2] != n_written || dst->ne[3] != 1 || dst->nb[0] != ggml_type_size(GGML_TYPE_F32) ||
++        dst->nb[1] != ggml_row_size(GGML_TYPE_F32, d) || ggml_nelements(src) != ggml_nelements(dst) ||
++        dst->nb[2] % sizeof(float) != 0) {
++        return std::nullopt;
++    }
++
++    const size_t slot_stride = k > 1 ? dst->nb[2] / sizeof(float) : 0;
++    const size_t cache_offset = ggml_webgpu_tensor_misalignment(ctx, dst) / sizeof(float);
++    if (slot_stride > UINT32_MAX || cache_offset > UINT32_MAX) {
++        return std::nullopt;
++    }
++
++    return ggml_webgpu_gdn_cache_fusion{ dst, (uint32_t) slot_stride, cpy_idx - node_idx + 1 };
++}
++#endif
++
+ static std::optional<webgpu_encoded_op> ggml_webgpu_set_rows(webgpu_context & ctx,
+                                                              ggml_tensor *    src,
+                                                              ggml_tensor *    idx,
+@@ -1564,10 +1714,211 @@ static webgpu_encoded_op ggml_webgpu_mul_mat(webgpu_context & ctx,
+     dispatches.push_back({
+         pipeline, std::move(params), std::move(entries), { wg_x, wg_y }
+     });
++#ifdef GGML_WEBGPU_GPU_PROFILE
++    if (use_mat_vec && src0->type == GGML_TYPE_Q1_0) {
++        dispatches.back().profile_name = pipeline.name + "_m" + std::to_string(dst->ne[0]) + "_k" +
++                                         std::to_string(src0->ne[0]);
++    }
++#endif
+ 
+     return ggml_backend_webgpu_build_multi(ctx, dispatches);
+ }
+ 
++#ifdef GGML_WEBGPU_Q1_GLU_FUSION
++static bool ggml_webgpu_can_fuse_q1_glu(const webgpu_context & ctx,
++                                        const ggml_cgraph *    cgraph,
++                                        int                    node_idx,
++                                        bool &                 computed_is_gate) {
++    if (node_idx < 1 || node_idx + 1 >= cgraph->n_nodes || !ctx->global_ctx->capabilities.supports_subgroups ||
++        ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup < GGML_WEBGPU_Q1_VEC4_WG_SIZE ||
++        cgraph->nodes[node_idx - 1]->op != GGML_OP_MUL_MAT ||
++        !ggml_can_fuse_subgraph(cgraph, node_idx, { GGML_OP_MUL_MAT, GGML_OP_GLU }, { node_idx + 1 })) {
++        return false;
++    }
++
++    const ggml_tensor * first = cgraph->nodes[node_idx - 1];
++    const ggml_tensor * computed = cgraph->nodes[node_idx];
++    const ggml_tensor * glu = cgraph->nodes[node_idx + 1];
++    const ggml_tensor * gate = nullptr;
++    const ggml_tensor * up = nullptr;
++    if (glu->src[0] == first && glu->src[1] == computed) {
++        gate = first;
++        up = computed;
++        computed_is_gate = false;
++    } else if (glu->src[0] == computed && glu->src[1] == first) {
++        gate = computed;
++        up = first;
++        computed_is_gate = true;
++    } else {
++        return false;
++    }
++
++    const ggml_tensor * gate_weights = gate->src[0];
++    const ggml_tensor * up_weights = up->src[0];
++    const ggml_tensor * input = gate->src[1];
++    if (up->src[1] != input || ggml_get_glu_op(glu) != GGML_GLU_OP_SWIGLU ||
++        ggml_get_op_params_i32(glu, 1) != 0 || gate_weights->type != GGML_TYPE_Q1_0 ||
++        up_weights->type != GGML_TYPE_Q1_0 || input->type != GGML_TYPE_F32 || gate->type != GGML_TYPE_F32 ||
++        up->type != GGML_TYPE_F32 || glu->type != GGML_TYPE_F32) {
++        return false;
++    }
++
++    const ggml_tensor * other = computed_is_gate ? up : gate;
++    const bool safe_other_overlap =
++        !ggml_webgpu_tensor_overlap(glu, other) || ggml_webgpu_tensor_equal(glu, other);
++    if (!ggml_are_same_shape(gate_weights, up_weights) || !ggml_are_same_stride(gate_weights, up_weights) ||
++        !ggml_are_same_shape(gate, up) || !ggml_are_same_shape(gate, glu) || ggml_nrows(input) != 1 ||
++        gate_weights->ne[2] != 1 || gate_weights->ne[3] != 1 || gate_weights->ne[0] != input->ne[0] ||
++        gate_weights->ne[1] != glu->ne[0] || gate_weights->ne[0] % ggml_blck_size(GGML_TYPE_Q1_0) != 0 ||
++        !ggml_is_contiguous(input) || !ggml_is_contiguous(other) || !ggml_is_contiguous(glu) || !safe_other_overlap) {
++        return false;
++    }
++
++    return !ggml_webgpu_tensor_overlap(glu, gate_weights) && !ggml_webgpu_tensor_overlap(glu, up_weights) &&
++           !ggml_webgpu_tensor_overlap(glu, input);
++}
++
++static webgpu_encoded_op ggml_webgpu_mul_mat_q1_glu(webgpu_context & ctx,
++                                                     ggml_tensor *    computed,
++                                                     ggml_tensor *    other,
++                                                     bool             computed_is_gate,
++                                                     ggml_tensor *    glu) {
++    ggml_tensor * weights = computed->src[0];
++    ggml_tensor * input = computed->src[1];
++
++    ggml_webgpu_shader_lib_context shader_lib_ctx = {};
++    shader_lib_ctx.src0 = weights;
++    shader_lib_ctx.src1 = input;
++    shader_lib_ctx.src2 = other;
++    shader_lib_ctx.dst = glu;
++    shader_lib_ctx.supports_subgroups = ctx->global_ctx->capabilities.supports_subgroups;
++    shader_lib_ctx.max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup;
++
++    webgpu_pipeline pipeline = ctx->shader_lib->get_mul_mat_vec_q1_glu_pipeline(shader_lib_ctx);
++    auto * decisions = static_cast<ggml_webgpu_mul_mat_vec_shader_decisions *>(pipeline.context.get());
++
++    std::vector<uint32_t> params = {
++        (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, weights) / ggml_type_size(weights->type)),
++        (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, input) / ggml_type_size(input->type)),
++        (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, other) / ggml_type_size(other->type)),
++        (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, glu) / ggml_type_size(glu->type)),
++        (uint32_t) glu->ne[0],
++        (uint32_t) weights->ne[0],
++        (uint32_t) (weights->nb[1] / ggml_type_size(weights->type)),
++        computed_is_gate ? 1u : 0u,
++    };
++    std::vector<wgpu::BindGroupEntry> entries = {
++        ggml_webgpu_make_tensor_bind_group_entry(ctx, 0, weights),
++        ggml_webgpu_make_tensor_bind_group_entry(ctx, 1, input),
++        ggml_webgpu_make_tensor_bind_group_entry(ctx, 2, other),
++        ggml_webgpu_make_tensor_bind_group_entry(ctx, 3, glu),
++    };
++
++    uint32_t wg_x;
++    uint32_t wg_y;
++    compute_2d_workgroups(CEIL_DIV((uint32_t) glu->ne[0], decisions->outputs_per_wg),
++                          ctx->global_ctx->capabilities.limits.maxComputeWorkgroupsPerDimension, wg_x, wg_y);
++    return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x, wg_y);
++}
++#endif
++
++#ifdef GGML_WEBGPU_Q1_DUAL_GLU_FUSION
++struct ggml_webgpu_q1_dual_glu_fusion {
++    ggml_tensor * gate;
++    ggml_tensor * up;
++    ggml_tensor * glu;
++};
++
++static std::optional<ggml_webgpu_q1_dual_glu_fusion> ggml_webgpu_try_q1_dual_glu_fusion(
++    const webgpu_context & ctx, const ggml_cgraph * cgraph, int node_idx) {
++    if (node_idx + 2 >= cgraph->n_nodes || !ctx->global_ctx->capabilities.supports_subgroups ||
++        ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup < GGML_WEBGPU_Q1_VEC4_WG_SIZE ||
++        !ggml_can_fuse_subgraph(cgraph, node_idx, { GGML_OP_MUL_MAT, GGML_OP_MUL_MAT, GGML_OP_GLU },
++                                { node_idx + 2 })) {
++        return std::nullopt;
++    }
++
++    ggml_tensor * first  = cgraph->nodes[node_idx];
++    ggml_tensor * second = cgraph->nodes[node_idx + 1];
++    ggml_tensor * glu    = cgraph->nodes[node_idx + 2];
++    ggml_tensor * gate   = nullptr;
++    ggml_tensor * up     = nullptr;
++    if (glu->src[0] == first && glu->src[1] == second) {
++        gate = first;
++        up   = second;
++    } else if (glu->src[0] == second && glu->src[1] == first) {
++        gate = second;
++        up   = first;
++    } else {
++        return std::nullopt;
++    }
++
++    ggml_tensor * gate_weights = gate->src[0];
++    ggml_tensor * up_weights   = up->src[0];
++    ggml_tensor * input        = gate->src[1];
++    if (gate_weights == nullptr || up_weights == nullptr || input == nullptr || up->src[1] != input ||
++        ggml_get_glu_op(glu) != GGML_GLU_OP_SWIGLU || ggml_get_op_params_i32(glu, 1) != 0 ||
++        gate_weights->type != GGML_TYPE_Q1_0 || up_weights->type != GGML_TYPE_Q1_0 ||
++        input->type != GGML_TYPE_F32 || gate->type != GGML_TYPE_F32 || up->type != GGML_TYPE_F32 ||
++        glu->type != GGML_TYPE_F32 || !ggml_are_same_shape(gate_weights, up_weights) ||
++        !ggml_are_same_stride(gate_weights, up_weights) || !ggml_are_same_shape(gate, up) ||
++        !ggml_are_same_shape(gate, glu) || ggml_nrows(input) != 1 || gate_weights->ne[2] != 1 ||
++        gate_weights->ne[3] != 1 || gate_weights->ne[0] != input->ne[0] || gate_weights->ne[1] != glu->ne[0] ||
++        gate_weights->ne[0] % ggml_blck_size(GGML_TYPE_Q1_0) != 0 || !ggml_is_contiguous(input) ||
++        !ggml_is_contiguous(glu)) {
++        return std::nullopt;
++    }
++
++    if (ggml_webgpu_tensor_overlap(glu, gate_weights) || ggml_webgpu_tensor_overlap(glu, up_weights) ||
++        ggml_webgpu_tensor_overlap(glu, input)) {
++        return std::nullopt;
++    }
++    return ggml_webgpu_q1_dual_glu_fusion{ gate, up, glu };
++}
++
++static webgpu_encoded_op ggml_webgpu_mul_mat_q1_dual_glu(webgpu_context &                         ctx,
++                                                          const ggml_webgpu_q1_dual_glu_fusion & fusion) {
++    ggml_tensor * gate_weights = fusion.gate->src[0];
++    ggml_tensor * up_weights   = fusion.up->src[0];
++    ggml_tensor * input        = fusion.gate->src[1];
++    ggml_tensor * glu          = fusion.glu;
++
++    ggml_webgpu_shader_lib_context shader_lib_ctx = {};
++    shader_lib_ctx.src0 = gate_weights;
++    shader_lib_ctx.src1 = up_weights;
++    shader_lib_ctx.src2 = input;
++    shader_lib_ctx.dst = glu;
++    shader_lib_ctx.supports_subgroups = ctx->global_ctx->capabilities.supports_subgroups;
++    shader_lib_ctx.supports_dot_product = ctx->global_ctx->capabilities.supports_dot_product;
++    shader_lib_ctx.max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup;
++
++    webgpu_pipeline pipeline = ctx->shader_lib->get_mul_mat_vec_q1_dual_glu_pipeline(shader_lib_ctx);
++    auto * decisions = static_cast<ggml_webgpu_mul_mat_vec_shader_decisions *>(pipeline.context.get());
++    std::vector<uint32_t> params = {
++        (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, gate_weights) / ggml_type_size(gate_weights->type)),
++        (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, up_weights) / ggml_type_size(up_weights->type)),
++        (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, input) / ggml_type_size(input->type)),
++        (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, glu) / ggml_type_size(glu->type)),
++        (uint32_t) glu->ne[0],
++        (uint32_t) gate_weights->ne[0],
++        (uint32_t) (gate_weights->nb[1] / ggml_type_size(gate_weights->type)),
++        (uint32_t) (up_weights->nb[1] / ggml_type_size(up_weights->type)),
++    };
++    std::vector<wgpu::BindGroupEntry> entries = {
++        ggml_webgpu_make_tensor_bind_group_entry(ctx, 0, gate_weights),
++        ggml_webgpu_make_tensor_bind_group_entry(ctx, 1, up_weights),
++        ggml_webgpu_make_tensor_bind_group_entry(ctx, 2, input),
++        ggml_webgpu_make_tensor_bind_group_entry(ctx, 3, glu),
++    };
++
++    uint32_t wg_x;
++    uint32_t wg_y;
++    compute_2d_workgroups(CEIL_DIV((uint32_t) glu->ne[0], decisions->outputs_per_wg),
++                          ctx->global_ctx->capabilities.limits.maxComputeWorkgroupsPerDimension, wg_x, wg_y);
++    return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x, wg_y);
++}
++#endif
++
+ static webgpu_encoded_op ggml_webgpu_mul_mat_id_vec(webgpu_context & ctx,
+                                                     ggml_tensor *    src0,
+                                                     ggml_tensor *    src1,
+@@ -3094,6 +3445,23 @@ static std::optional<webgpu_encoded_op> ggml_webgpu_encode(webgpu_context ctx,
+         case GGML_OP_GET_ROWS:
+             return ggml_webgpu_get_rows(ctx, src0, src1, node);
+         case GGML_OP_MUL_MAT:
++#ifdef GGML_WEBGPU_Q1_DUAL_GLU_FUSION
++            if (auto fusion = ggml_webgpu_try_q1_dual_glu_fusion(ctx, cgraph, node_idx)) {
++                num_encoded_ops = 3;
++                return ggml_webgpu_mul_mat_q1_dual_glu(ctx, *fusion);
++            }
++#endif
++#ifdef GGML_WEBGPU_Q1_GLU_FUSION
++            {
++                bool computed_is_gate = false;
++                if (ggml_webgpu_can_fuse_q1_glu(ctx, cgraph, node_idx, computed_is_gate)) {
++                    num_encoded_ops = 2;
++                    ggml_tensor * glu = nodes[node_idx + 1];
++                    ggml_tensor * other = computed_is_gate ? glu->src[1] : glu->src[0];
++                    return ggml_webgpu_mul_mat_q1_glu(ctx, node, other, computed_is_gate, glu);
++                }
++            }
++#endif
+             return ggml_webgpu_mul_mat(ctx, src0, src1, node);
+         case GGML_OP_MUL_MAT_ID:
+             return ggml_webgpu_mul_mat_id(ctx, src0, src1, src2, node);
+@@ -3148,6 +3516,13 @@ static std::optional<webgpu_encoded_op> ggml_webgpu_encode(webgpu_context ctx,
+             return ggml_webgpu_ssm_scan(ctx, src0, src1, src2, node->src[3], node->src[4], node->src[5], node->src[6],
+                                         node);
+         case GGML_OP_GATED_DELTA_NET:
++#ifdef GGML_WEBGPU_GDN_CACHE_FUSION
++            if (auto fusion = ggml_webgpu_try_gdn_cache_fusion(ctx, cgraph, node_idx)) {
++                num_encoded_ops = fusion->num_encoded_ops;
++                return ggml_webgpu_gated_delta_net(ctx, src0, src1, src2, node->src[3], node->src[4], node->src[5],
++                                                   node, fusion->cache, fusion->slot_stride);
++            }
++#endif
+             return ggml_webgpu_gated_delta_net(ctx, src0, src1, src2, node->src[3], node->src[4], node->src[5], node);
+         case GGML_OP_PAD:
+             return ggml_webgpu_pad(ctx, src0, node);
+@@ -3201,6 +3576,7 @@ static void ggml_backend_webgpu_collect_profile_results(webgpu_context &
+         // WebGPU timestamps are in ns; convert to ms.
+         const double elapsed_ms = double(ts_data[2 * i + 1] - ts_data[2 * i]) * 1e-6;
+         ctx->shader_gpu_time_ms[pipeline_names[i]] += elapsed_ms;
++        ctx->profile_graph_gpu_time_ms[pipeline_names[i]] += elapsed_ms;
+     }
+ 
+     ctx->profile_timestamp_host_buf.Unmap();
+@@ -3244,9 +3620,19 @@ static ggml_status ggml_backend_webgpu_graph_compute(ggml_backend_t backend, str
+     bool     contains_set_rows    = false;
+     int      num_encoded_ops      = 1;
+     int      node_idx             = 0;
++#ifdef GGML_WEBGPU_TURBO_TRACE
++    uint32_t num_param_uploads    = 0;
++    uint32_t num_total_kernels    = 0;
++    uint32_t num_submit_batches   = 0;
++#endif
++
++#ifdef GGML_WEBGPU_TURBO_TRACE
++    const auto turbo_trace_started_at = std::chrono::steady_clock::now();
++#endif
+ 
+ #ifdef GGML_WEBGPU_GPU_PROFILE
+     ctx->profile_timestamp_query_count = 0;
++    ctx->profile_graph_gpu_time_ms.clear();
+     std::vector<std::string> profile_pipeline_names;
+ #endif
+ 
+@@ -3262,6 +3648,12 @@ static ggml_status ggml_backend_webgpu_graph_compute(ggml_backend_t backend, str
+         if (auto cmd = ggml_webgpu_encode(ctx, cgraph, node_idx, num_encoded_ops)) {
+             commands.push_back(*cmd);
+             num_batched_kernels += cmd.value().num_kernels;
++#ifdef GGML_WEBGPU_TURBO_TRACE
++            num_total_kernels += cmd.value().num_kernels;
++#ifndef GGML_WEBGPU_BATCH_PARAM_UPLOADS
++            num_param_uploads += cmd.value().num_kernels;
++#endif
++#endif
+ #ifdef GGML_WEBGPU_GPU_PROFILE
+             profile_pipeline_names.insert(profile_pipeline_names.end(), cmd->pipeline_names.begin(),
+                                           cmd->pipeline_names.end());
+@@ -3272,9 +3664,19 @@ static ggml_status ggml_backend_webgpu_graph_compute(ggml_backend_t backend, str
+             if (ctx->active_compute_pass) {
+                 ctx->active_compute_pass.End();
+             }
++#ifdef GGML_WEBGPU_BATCH_PARAM_UPLOADS
++            if (ctx->param_arena.flush(ctx->global_ctx->queue)) {
++#ifdef GGML_WEBGPU_TURBO_TRACE
++                num_param_uploads++;
++#endif
++            }
++#endif
+             num_batched_kernels                = 0;
+             wgpu::CommandBuffer batch_commands = ctx->active_command_encoder.Finish();
+             ggml_backend_webgpu_submit_commands(ctx, batch_commands, num_inflight_batches);
++#ifdef GGML_WEBGPU_TURBO_TRACE
++            num_submit_batches++;
++#endif
+ 
+             // reset state for next batch
+             ctx->active_command_encoder = ctx->global_ctx->device.CreateCommandEncoder();
+@@ -3305,8 +3707,18 @@ static ggml_status ggml_backend_webgpu_graph_compute(ggml_backend_t backend, str
+     }
+ 
+     if (num_batched_kernels > 0) {
++#ifdef GGML_WEBGPU_BATCH_PARAM_UPLOADS
++        if (ctx->param_arena.flush(ctx->global_ctx->queue)) {
++#ifdef GGML_WEBGPU_TURBO_TRACE
++            num_param_uploads++;
++#endif
++        }
++#endif
+         wgpu::CommandBuffer batch_commands = ctx->active_command_encoder.Finish();
+         ggml_backend_webgpu_submit_commands(ctx, batch_commands, num_inflight_batches);
++#ifdef GGML_WEBGPU_TURBO_TRACE
++        num_submit_batches++;
++#endif
+         ctx->param_arena.reset();
+         commands.clear();
+     }
+@@ -3314,6 +3726,27 @@ static ggml_status ggml_backend_webgpu_graph_compute(ggml_backend_t backend, str
+ 
+ #ifdef GGML_WEBGPU_GPU_PROFILE
+     ggml_backend_webgpu_collect_profile_results(ctx, profile_pipeline_names, num_inflight_batches);
++
++    const uint64_t profile_graph_count = ++ctx->profile_graph_count;
++    double         profile_graph_total_ms = 0.0;
++    std::vector<std::pair<std::string, double>> profile_graph_pipelines;
++    profile_graph_pipelines.reserve(ctx->profile_graph_gpu_time_ms.size());
++    for (const auto & kv : ctx->profile_graph_gpu_time_ms) {
++        profile_graph_total_ms += kv.second;
++        profile_graph_pipelines.push_back(kv);
++    }
++    std::sort(profile_graph_pipelines.begin(), profile_graph_pipelines.end(),
++              [](const auto & lhs, const auto & rhs) { return lhs.second > rhs.second; });
++    std::cout << "@@WEBGPU_TRACE@@gpu_profile graph=" << profile_graph_count << " total_ms=" << std::fixed
++              << std::setprecision(3) << profile_graph_total_ms << " top=";
++    const size_t profile_top_count = std::min<size_t>(16, profile_graph_pipelines.size());
++    for (size_t i = 0; i < profile_top_count; ++i) {
++        if (i > 0) {
++            std::cout << ",";
++        }
++        std::cout << profile_graph_pipelines[i].first << ":" << profile_graph_pipelines[i].second;
++    }
++    std::cout << std::endl;
+ #endif
+ 
+     if (contains_set_rows) {
+@@ -3321,6 +3754,20 @@ static ggml_status ggml_backend_webgpu_graph_compute(ggml_backend_t backend, str
+     }
+ 
+     WEBGPU_CPU_PROFILE_TOTAL_END(graph_compute, ctx->global_ctx);
++
++#ifdef GGML_WEBGPU_TURBO_TRACE
++    const uint64_t graph_count = ++ctx->turbo_trace_graph_count;
++    if (graph_count <= 16 || (graph_count & (graph_count - 1)) == 0) {
++        const auto elapsed_us = std::chrono::duration_cast<std::chrono::microseconds>(
++                                    std::chrono::steady_clock::now() - turbo_trace_started_at)
++                                    .count();
++        std::cout << "@@WEBGPU_TRACE@@turbo_graph graph=" << graph_count << " nodes=" << cgraph->n_nodes
++                  << " kernels=" << num_total_kernels << " submissions=" << num_submit_batches
++                  << " parameter_uploads=" << num_param_uploads
++                  << " batch_size=" << ctx->global_ctx->command_submit_batch_size << " encode_us=" << elapsed_us
++                  << std::endl;
++    }
++#endif
+     return GGML_STATUS_SUCCESS;
+ }
+ 
+@@ -3376,6 +3823,44 @@ static void ggml_backend_webgpu_event_wait(ggml_backend_t backend, ggml_backend_
+     ggml_backend_webgpu_device_event_synchronize(nullptr, event);
+ }
+ 
++#ifdef GGML_WEBGPU_Q1_ROW_RETILE
++static const void * ggml_webgpu_prepare_q1_row_retiled_data(const ggml_tensor * tensor,
++                                                            const void *        data,
++                                                            size_t              offset,
++                                                            size_t              size,
++                                                            std::vector<uint8_t> & retiled) {
++    if (tensor->type != GGML_TYPE_Q1_0) {
++        return data;
++    }
++    if (offset != 0 || size != ggml_nbytes(tensor) || !ggml_is_contiguous(tensor)) {
++        GGML_ABORT("ggml_webgpu: Q1_0 row retiling requires a full contiguous tensor upload\n");
++    }
++
++    const size_t blocks_per_row = tensor->ne[0] / 128;
++    const size_t row_bytes      = blocks_per_row * 18;
++    const size_t rows           = ggml_nrows(tensor);
++    GGML_ASSERT(tensor->ne[0] % 128 == 0);
++    GGML_ASSERT(blocks_per_row % 2 == 0);
++    GGML_ASSERT(size == rows * row_bytes);
++
++    const uint8_t * src = static_cast<const uint8_t *>(data);
++    retiled.resize(size);
++    for (size_t row = 0; row < rows; ++row) {
++        const uint8_t * src_row = src + row * row_bytes;
++        uint8_t *       dst_row = retiled.data() + row * row_bytes;
++        for (size_t block_pair = 0; block_pair < blocks_per_row / 2; ++block_pair) {
++            const uint8_t * src_pair = src_row + block_pair * 36;
++            uint8_t *       dst_pair = dst_row + block_pair * 36;
++            memcpy(dst_pair, src_pair + 2, 16);
++            memcpy(dst_pair + 16, src_pair + 20, 16);
++            memcpy(dst_pair + 32, src_pair, 2);
++            memcpy(dst_pair + 34, src_pair + 18, 2);
++        }
++    }
++    return retiled.data();
++}
++#endif
++
+ static void ggml_backend_webgpu_set_tensor_async(ggml_backend_t backend,
+                                                  ggml_tensor *  tensor,
+                                                  const void *   data,
+@@ -3385,6 +3870,11 @@ static void ggml_backend_webgpu_set_tensor_async(ggml_backend_t backend,
+     auto * buf_ctx      = (ggml_backend_webgpu_buffer_context *) tensor->buffer->context;
+     size_t total_offset = ggml_webgpu_tensor_offset(tensor) + offset;
+ 
++#ifdef GGML_WEBGPU_Q1_ROW_RETILE
++    std::vector<uint8_t> retiled;
++    data = ggml_webgpu_prepare_q1_row_retiled_data(tensor, data, offset, size, retiled);
++#endif
++
+     // Write aligned portion
+     buf_ctx->global_ctx->queue.WriteBuffer(buf_ctx->buffer, total_offset, data, (size / 4) * 4);
+ 
+@@ -3486,6 +3976,11 @@ static void ggml_backend_webgpu_buffer_set_tensor(ggml_backend_buffer_t buffer,
+ 
+     size_t total_offset = ggml_webgpu_tensor_offset(tensor) + offset;
+ 
++#ifdef GGML_WEBGPU_Q1_ROW_RETILE
++    std::vector<uint8_t> retiled;
++    data = ggml_webgpu_prepare_q1_row_retiled_data(tensor, data, offset, size, retiled);
++#endif
++
+     buf_ctx->global_ctx->queue.WriteBuffer(buf_ctx->buffer, total_offset, data, (size / 4) * 4);
+ 
+     if (size % 4 != 0) {
+diff --git a/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/common_decls.tmpl b/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/common_decls.tmpl
+index 6634fbd..97227b0 100644
+--- a/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/common_decls.tmpl
++++ b/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/common_decls.tmpl
+@@ -25,6 +25,10 @@ fn load_u32_at_src(byte_offset: u32) -> u32 {
+     return select(shifted, lo, shift == 0u);
+ }
+ 
++fn load_u32_at_src_aligned(byte_offset: u32) -> u32 {
++    return src[byte_offset / 4u];
++}
++
+ fn load_f16_at_src(byte_offset: u32) -> f16 {
+     let packed = unpack2x16float(load_u16_at_src(byte_offset));
+     return f16(packed[0]);
+diff --git a/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/gated_delta_net.wgsl b/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/gated_delta_net.wgsl
+index 7d7b347..d3a04d5 100644
+--- a/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/gated_delta_net.wgsl
++++ b/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/gated_delta_net.wgsl
+@@ -19,6 +19,11 @@ var<storage, read_write> src_state: array<f32>;
+ @group(0) @binding(6)
+ var<storage, read_write> dst: array<f32>;
+ 
++#ifdef FUSED_CACHE
++@group(0) @binding(7)
++var<storage, read_write> state_cache: array<f32>;
++#endif
++
+ struct Params {
+     h: u32,
+     n_tokens: u32,
+@@ -41,9 +46,15 @@ struct Params {
+     rq3: u32,
+     K: u32,
+     scale: f32,
++    cache_offset: u32,
++    cache_slot_stride: u32,
+ };
+ 
++#ifdef FUSED_CACHE
++@group(0) @binding(8)
++#else
+ @group(0) @binding(7)
++#endif
+ var<uniform> params: Params;
+ 
+ var<workgroup> sh_k: array<f32, S_V>;
+@@ -131,9 +142,17 @@ fn main(
+             // snapshot slot mapping: slot 0 = most recent state, slot s = s tokens back.
+             let target_slot = i32(params.n_tokens) - 1 - i32(t);
+             if (target_slot >= 0 && target_slot < i32(params.K)) {
++                #ifdef FUSED_CACHE
++                let slot_base = params.cache_offset + u32(target_slot) * params.cache_slot_stride + state_out_base;
++                #else
+                 let slot_base = params.s_off + u32(target_slot) * state_size_per_snap + state_out_base;
++                #endif
+                 for (var i = 0u; i < S_V; i++) {
++                    #ifdef FUSED_CACHE
++                    state_cache[slot_base + col * S_V + i] = state[i];
++                    #else
+                     dst[slot_base + col * S_V + i] = state[i];
++                    #endif
+                 }
+             }
+         }
+@@ -143,7 +162,11 @@ fn main(
+ 
+     if (params.K == 1u) {
+         for (var i = 0u; i < S_V; i++) {
++            #ifdef FUSED_CACHE
++            state_cache[params.cache_offset + state_out_base + col * S_V + i] = state[i];
++            #else
+             dst[params.s_off + state_out_base + col * S_V + i] = state[i];
++            #endif
+         }
+     }
+ }
+diff --git a/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/get_rows.wgsl b/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/get_rows.wgsl
+index 487edb3..e671b03 100644
+--- a/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/get_rows.wgsl
++++ b/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/get_rows.wgsl
+@@ -29,10 +29,23 @@ fn copy_elements(src_base: u32, dst_base: u32, offset: u32) {
+ 
+ #ifdef Q1_0
+ fn copy_elements(src_base: u32, dst_base: u32, offset: u32) {
++#ifdef Q1_ROW_RETILED
++    let row_byte_base = src_base * 18u;
++    let block_in_pair = offset & 1u;
++    let pair_byte_base = row_byte_base + (offset / 2u) * 36u;
++    let d = load_f16_as_f32_at_src(pair_byte_base + 32u + block_in_pair * 2u);
++#else
+     let block_byte_base = (src_base + offset) * 18;
+     let d = load_f16_as_f32_at_src(block_byte_base);
++#endif
+     for (var j: u32 = 0u; j < 4u; j++) {
++#ifdef Q1_ROW_RETILED
++        let q_packed = load_u32_at_src_aligned(
++            pair_byte_base + block_in_pair * 16u + j * 4u
++        );
++#else
+         let q_packed = load_u32_at_src(block_byte_base + 2u + j * 4u);
++#endif
+         let dst_base128 = dst_base + offset * 128u + j * 32u;
+         for (var k: u32 = 0; k < 4u; k++) {
+             let q_byte = get_byte(q_packed, k);
+diff --git a/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_decls.tmpl b/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_decls.tmpl
+index 13996ab..9329fd3 100644
+--- a/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_decls.tmpl
++++ b/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_decls.tmpl
+@@ -84,9 +84,19 @@ fn init_shmem_src0(thread_id: u32, batch_offset: u32, offset_m: u32, k_outer: u3
+         let block_k = global_k_start / BLOCK_SIZE;
+         let byte_in_block = (global_k_start % BLOCK_SIZE) / 8u;
+         let src0_idx = batch_offset + global_m * params.stride_01 + block_k;
++#ifdef Q1_ROW_RETILED
++        let row_byte_base = (batch_offset + global_m * params.stride_01) * BLOCK_SIZE_BYTES;
++        let block_in_pair = block_k & 1u;
++        let pair_byte_base = row_byte_base + (block_k / 2u) * 36u;
++        let d = load_f16_at_src0(pair_byte_base + 32u + block_in_pair * 2u);
++        let q_byte_offset = block_in_pair * 16u + byte_in_block;
++        let q_word = load_u32_at_src0_aligned(pair_byte_base + q_byte_offset);
++        let q_byte = (q_word >> ((q_byte_offset & 3u) * 8u)) & 0xFFu;
++#else
+         let block_byte_base = src0_idx * BLOCK_SIZE_BYTES;
+         let d = load_f16_at_src0(block_byte_base);
+         let q_byte = load_u32_at_src0(block_byte_base + 2u + byte_in_block) & 0xFFu;
++#endif
+ 
+         for (var bit = 0u; bit < NQ; bit++) {
+             let global_k = global_k_start + bit;
+diff --git a/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_vec.wgsl b/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_vec.wgsl
+index ebdf095..c989c8c 100644
+--- a/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_vec.wgsl
++++ b/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_vec.wgsl
+@@ -101,6 +101,14 @@ fn main(
+     for (var col = 0u;col < NUM_COLS;col += 1) {
+ 
+ #ifdef USE_SUBGROUP_REDUCTION
++#ifdef USE_SINGLE_SUBGROUP_REDUCTION
++            for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
++                let row_total = subgroupAdd(acc[col][row]);
++                if (subgroup_invocation_id == 0u && row_base + row < params.m) {
++                    dst[dst_idx_base + col * params.m + row] = row_total;
++                }
++            }
++#else
+             for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
+                 let subgroup_total = subgroupAdd(acc[col][row]);
+                 if (subgroup_invocation_id == 0u) {
+@@ -122,6 +130,7 @@ fn main(
+                 }
+             }
+ #endif
++#endif
+ 
+ #ifdef USE_WORKGROUP_REDUCTION
+             for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
+@@ -151,7 +160,9 @@ fn main(
+             }
+ #endif
+ 
+-    workgroupBarrier();
++#ifndef USE_SINGLE_SUBGROUP_REDUCTION
++            workgroupBarrier();
++#endif
+ 
+     }
+ }
 diff --git a/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_vec_acc.tmpl b/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_vec_acc.tmpl
-index 8fd0d19..f12848f 100644
+index 8fd0d19..3bf03a9 100644
 --- a/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_vec_acc.tmpl
 +++ b/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_vec_acc.tmpl
-@@ -61,6 +61,58 @@ fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src
+@@ -61,6 +61,68 @@ fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src
  }
  #endif
  
@@ -558,9 +1853,19 @@ index 8fd0d19..f12848f 100644
 +        for (var row = 0u; row < OUTPUTS_PER_WG; row += 1u) {
 +            let output_row = row_base + row;
 +            if (output_row < params.m) {
++#ifdef Q1_ROW_RETILED
++                let row_byte_base = (src0_batch_offset + output_row * params.stride_01) * BLOCK_SIZE_BYTES;
++                let block_in_pair = block & 1u;
++                let pair_byte_base = row_byte_base + (block / 2u) * 36u;
++                let d = f32(load_f16_at_src0(pair_byte_base + 32u + block_in_pair * 2u));
++                let q_word = load_u32_at_src0_aligned(
++                    pair_byte_base + block_in_pair * 16u + thread_within_block * 4u
++                );
++#else
 +                let block_byte_base = (src0_batch_offset + output_row * params.stride_01 + block) * BLOCK_SIZE_BYTES;
 +                let d = f32(load_f16_at_src0(block_byte_base));
 +                let q_word = load_u32_at_src0(block_byte_base + 2u + thread_within_block * 4u);
++#endif
 +                for (var col = 0u; col < NUM_COLS; col += 1u) {
 +                    var row_sum = 0.0;
 +                    for (var i = 0u; i < VECS_PER_THREAD; i += 1u) {
@@ -579,3 +1884,327 @@ index 8fd0d19..f12848f 100644
  #ifdef MUL_ACC_Q1_0
  #define BLOCK_SIZE 128
  #define BLOCK_SIZE_BYTES 18
+@@ -82,9 +144,19 @@ fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src
+         for (var row = 0u; row < OUTPUTS_PER_WG; row++) {
+             let output_row = row_base + row;
+             if (output_row < params.m) {
++#ifdef Q1_ROW_RETILED
++                let row_byte_base = (src0_batch_offset + output_row * params.stride_01) * BLOCK_SIZE_BYTES;
++                let block_in_pair = block & 1u;
++                let pair_byte_base = row_byte_base + (block / 2u) * 36u;
++                let d = f32(load_f16_at_src0(pair_byte_base + 32u + block_in_pair * 2u));
++                let q_byte_offset = block_in_pair * 16u + thread_within_block;
++                let q_word = load_u32_at_src0_aligned(pair_byte_base + q_byte_offset);
++                let q_byte = (q_word >> ((q_byte_offset & 3u) * 8u)) & 0xFFu;
++#else
+                 let block_byte_base = (src0_batch_offset + output_row * params.stride_01 + block) * BLOCK_SIZE_BYTES;
+                 let d = f32(load_f16_at_src0(block_byte_base));
+                 let q_byte = load_u32_at_src0(block_byte_base + 2u + thread_within_block) & 0xFFu;
++#endif
+                 for (var col = 0u;col < NUM_COLS;col += 1) {
+                     var row_sum = 0.0;
+                     for (var bit = 0u; bit < 8u; bit++) {
+diff --git a/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_vec_q1_glu.wgsl b/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_vec_q1_glu.wgsl
+new file mode 100644
+index 0000000..9d86a26
+--- /dev/null
++++ b/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_vec_q1_glu.wgsl
+@@ -0,0 +1,141 @@
++enable subgroups;
++
++struct Params {
++    offset_weights: u32,
++    offset_input: u32,
++    offset_other: u32,
++    offset_dst: u32,
++    m: u32,
++    k: u32,
++    stride_weights_01: u32,
++    computed_is_gate: u32,
++};
++
++@group(0) @binding(0) var<storage, read_write> weights: array<u32>;
++@group(0) @binding(1) var<storage, read_write> input: array<f32>;
++@group(0) @binding(2) var<storage, read_write> other: array<f32>;
++@group(0) @binding(3) var<storage, read_write> dst: array<f32>;
++@group(0) @binding(4) var<uniform> params: Params;
++
++var<workgroup> partial_sums: array<f32, OUTPUTS_PER_WG * WG_SIZE>;
++
++fn partial_index(row: u32, subgroup: u32) -> u32 {
++    return row * WG_SIZE + subgroup;
++}
++
++fn load_u16_weights(byte_offset: u32) -> u32 {
++    let word = weights[byte_offset / 4u];
++    let shift = (byte_offset & 0x2u) * 8u;
++    return (word >> shift) & 0xFFFFu;
++}
++
++fn load_u32_weights(byte_offset: u32) -> u32 {
++    let word_idx = byte_offset / 4u;
++    let shift = (byte_offset & 0x3u) * 8u;
++    let lo = weights[word_idx];
++    let hi = weights[word_idx + 1u];
++    return select((lo >> shift) | (hi << (32u - shift)), lo, shift == 0u);
++}
++
++fn load_f16_weights(byte_offset: u32) -> f32 {
++    return unpack2x16float(load_u16_weights(byte_offset))[0];
++}
++
++fn q1_signs4(word: u32, shift: u32) -> vec4<f32> {
++    let bit_offsets = vec4<u32>(shift, shift + 1u, shift + 2u, shift + 3u);
++    let bits = (vec4<u32>(word) >> bit_offsets) & vec4<u32>(1u);
++    return select(vec4<f32>(-1.0), vec4<f32>(1.0), bits == vec4<u32>(1u));
++}
++
++fn swiglu(gate: f32, up: f32) -> f32 {
++    return gate / (1.0 + exp(-gate)) * up;
++}
++
++@compute @workgroup_size(WG_SIZE)
++fn main(
++    @builtin(local_invocation_id) local_id: vec3<u32>,
++    @builtin(workgroup_id) wg_id: vec3<u32>,
++    @builtin(subgroup_id) subgroup_id: u32,
++    @builtin(subgroup_invocation_id) subgroup_invocation_id: u32,
++    @builtin(num_subgroups) num_subgroups: u32,
++    @builtin(subgroup_size) subgroup_size: u32,
++) {
++    const BLOCK_SIZE: u32 = 128u;
++    const BLOCK_SIZE_BYTES: u32 = 18u;
++    const THREADS_PER_BLOCK: u32 = 4u;
++    const VECS_PER_THREAD: u32 = 8u;
++
++    let thread_id = local_id.x;
++    let row_base = wg_id.x * OUTPUTS_PER_WG;
++    let num_blocks = params.k / BLOCK_SIZE;
++    let thread_within_block = thread_id % THREADS_PER_BLOCK;
++    var acc: array<f32, OUTPUTS_PER_WG>;
++
++    for (
++        var block = thread_id / THREADS_PER_BLOCK;
++        block < num_blocks;
++        block += WG_SIZE / THREADS_PER_BLOCK
++    ) {
++        let x_base = params.offset_input + block * BLOCK_SIZE + thread_within_block * 32u;
++        var x_block: array<vec4<f32>, VECS_PER_THREAD>;
++        for (var i = 0u; i < VECS_PER_THREAD; i += 1u) {
++            let x = x_base + i * 4u;
++            x_block[i] = vec4<f32>(input[x], input[x + 1u], input[x + 2u], input[x + 3u]);
++        }
++
++        for (var row = 0u; row < OUTPUTS_PER_WG; row += 1u) {
++            let output_row = row_base + row;
++            if (output_row < params.m) {
++#ifdef Q1_ROW_RETILED
++                let row_byte_base =
++                    (params.offset_weights + output_row * params.stride_weights_01) * BLOCK_SIZE_BYTES;
++                let block_in_pair = block & 1u;
++                let pair_byte_base = row_byte_base + (block / 2u) * 36u;
++                let d = load_f16_weights(pair_byte_base + 32u + block_in_pair * 2u);
++                let q = load_u32_weights(
++                    pair_byte_base + block_in_pair * 16u + thread_within_block * 4u
++                );
++#else
++                let block_byte_base =
++                    (params.offset_weights + output_row * params.stride_weights_01 + block) * BLOCK_SIZE_BYTES;
++                let d = load_f16_weights(block_byte_base);
++                let q = load_u32_weights(block_byte_base + 2u + thread_within_block * 4u);
++#endif
++                var row_sum = 0.0;
++                for (var i = 0u; i < VECS_PER_THREAD; i += 1u) {
++                    row_sum += d * dot(x_block[i], q1_signs4(q, i * 4u));
++                }
++                acc[row] += row_sum;
++            }
++        }
++    }
++
++    for (var row = 0u; row < OUTPUTS_PER_WG; row += 1u) {
++        let subgroup_total = subgroupAdd(acc[row]);
++        if (subgroup_invocation_id == 0u) {
++            partial_sums[partial_index(row, subgroup_id)] = subgroup_total;
++        }
++    }
++
++    workgroupBarrier();
++
++    for (
++        var row = subgroup_id;
++        row < OUTPUTS_PER_WG && row_base + row < params.m;
++        row += num_subgroups
++    ) {
++        var row_acc = 0.0;
++        for (var subgroup = subgroup_invocation_id; subgroup < num_subgroups; subgroup += subgroup_size) {
++            row_acc += partial_sums[partial_index(row, subgroup)];
++        }
++        let computed = subgroupAdd(row_acc);
++        if (subgroup_invocation_id == 0u) {
++            let index = row_base + row;
++            let other_value = other[params.offset_other + index];
++            let computed_is_gate = params.computed_is_gate != 0u;
++            let gate = select(other_value, computed, computed_is_gate);
++            let up = select(computed, other_value, computed_is_gate);
++            dst[params.offset_dst + index] = swiglu(gate, up);
++        }
++    }
++}
+diff --git a/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_vec_q1_dual_glu.wgsl b/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_vec_q1_dual_glu.wgsl
+new file mode 100644
+index 0000000..0ae71e8
+--- /dev/null
++++ b/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_vec_q1_dual_glu.wgsl
+@@ -0,0 +1,151 @@
++enable subgroups;
++
++struct Params {
++    offset_gate_weights: u32,
++    offset_up_weights: u32,
++    offset_input: u32,
++    offset_dst: u32,
++    m: u32,
++    k: u32,
++    stride_gate_weights_01: u32,
++    stride_up_weights_01: u32,
++};
++
++@group(0) @binding(0) var<storage, read_write> gate_weights: array<u32>;
++@group(0) @binding(1) var<storage, read_write> up_weights: array<u32>;
++@group(0) @binding(2) var<storage, read_write> input: array<f32>;
++@group(0) @binding(3) var<storage, read_write> dst: array<f32>;
++@group(0) @binding(4) var<uniform> params: Params;
++
++var<workgroup> partial_gate: array<f32, OUTPUTS_PER_WG * WG_SIZE>;
++var<workgroup> partial_up: array<f32, OUTPUTS_PER_WG * WG_SIZE>;
++
++fn partial_index(row: u32, subgroup: u32) -> u32 {
++    return row * WG_SIZE + subgroup;
++}
++
++fn load_u16_gate(byte_offset: u32) -> u32 {
++    let word = gate_weights[byte_offset / 4u];
++    let shift = (byte_offset & 0x2u) * 8u;
++    return (word >> shift) & 0xFFFFu;
++}
++
++fn load_u16_up(byte_offset: u32) -> u32 {
++    let word = up_weights[byte_offset / 4u];
++    let shift = (byte_offset & 0x2u) * 8u;
++    return (word >> shift) & 0xFFFFu;
++}
++
++fn load_u32_gate(byte_offset: u32) -> u32 {
++    let word_idx = byte_offset / 4u;
++    let shift = (byte_offset & 0x3u) * 8u;
++    let lo = gate_weights[word_idx];
++    let hi = gate_weights[word_idx + 1u];
++    return select((lo >> shift) | (hi << (32u - shift)), lo, shift == 0u);
++}
++
++fn load_u32_up(byte_offset: u32) -> u32 {
++    let word_idx = byte_offset / 4u;
++    let shift = (byte_offset & 0x3u) * 8u;
++    let lo = up_weights[word_idx];
++    let hi = up_weights[word_idx + 1u];
++    return select((lo >> shift) | (hi << (32u - shift)), lo, shift == 0u);
++}
++
++fn q1_signs4(word: u32, shift: u32) -> vec4<f32> {
++    let bit_offsets = vec4<u32>(shift, shift + 1u, shift + 2u, shift + 3u);
++    let bits = (vec4<u32>(word) >> bit_offsets) & vec4<u32>(1u);
++    return select(vec4<f32>(-1.0), vec4<f32>(1.0), bits == vec4<u32>(1u));
++}
++
++fn swiglu(gate: f32, up: f32) -> f32 {
++    return gate / (1.0 + exp(-gate)) * up;
++}
++
++@compute @workgroup_size(WG_SIZE)
++fn main(
++    @builtin(local_invocation_id) local_id: vec3<u32>,
++    @builtin(workgroup_id) wg_id: vec3<u32>,
++    @builtin(subgroup_id) subgroup_id: u32,
++    @builtin(subgroup_invocation_id) subgroup_invocation_id: u32,
++    @builtin(num_subgroups) num_subgroups: u32,
++    @builtin(subgroup_size) subgroup_size: u32,
++) {
++    const BLOCK_SIZE: u32 = 128u;
++    const BLOCK_SIZE_BYTES: u32 = 18u;
++    const THREADS_PER_BLOCK: u32 = 4u;
++    const VECS_PER_THREAD: u32 = 8u;
++
++    let thread_id = local_id.x;
++    let row_base = wg_id.x * OUTPUTS_PER_WG;
++    let num_blocks = params.k / BLOCK_SIZE;
++    let thread_within_block = thread_id % THREADS_PER_BLOCK;
++    var gate_acc: array<f32, OUTPUTS_PER_WG>;
++    var up_acc: array<f32, OUTPUTS_PER_WG>;
++
++    for (
++        var block = thread_id / THREADS_PER_BLOCK;
++        block < num_blocks;
++        block += WG_SIZE / THREADS_PER_BLOCK
++    ) {
++        let x_base = params.offset_input + block * BLOCK_SIZE + thread_within_block * 32u;
++        var x_block: array<vec4<f32>, VECS_PER_THREAD>;
++        for (var i = 0u; i < VECS_PER_THREAD; i += 1u) {
++            let x = x_base + i * 4u;
++            x_block[i] = vec4<f32>(input[x], input[x + 1u], input[x + 2u], input[x + 3u]);
++        }
++
++        for (var row = 0u; row < OUTPUTS_PER_WG; row += 1u) {
++            let output_row = row_base + row;
++            if (output_row < params.m) {
++                let gate_byte_base =
++                    (params.offset_gate_weights + output_row * params.stride_gate_weights_01 + block) *
++                    BLOCK_SIZE_BYTES;
++                let up_byte_base =
++                    (params.offset_up_weights + output_row * params.stride_up_weights_01 + block) *
++                    BLOCK_SIZE_BYTES;
++                let gate_d = unpack2x16float(load_u16_gate(gate_byte_base))[0];
++                let up_d = unpack2x16float(load_u16_up(up_byte_base))[0];
++                let gate_q = load_u32_gate(gate_byte_base + 2u + thread_within_block * 4u);
++                let up_q = load_u32_up(up_byte_base + 2u + thread_within_block * 4u);
++                var gate_sum = 0.0;
++                var up_sum = 0.0;
++                for (var i = 0u; i < VECS_PER_THREAD; i += 1u) {
++                    gate_sum += gate_d * dot(x_block[i], q1_signs4(gate_q, i * 4u));
++                    up_sum += up_d * dot(x_block[i], q1_signs4(up_q, i * 4u));
++                }
++                gate_acc[row] += gate_sum;
++                up_acc[row] += up_sum;
++            }
++        }
++    }
++
++    for (var row = 0u; row < OUTPUTS_PER_WG; row += 1u) {
++        let gate_total = subgroupAdd(gate_acc[row]);
++        let up_total = subgroupAdd(up_acc[row]);
++        if (subgroup_invocation_id == 0u) {
++            partial_gate[partial_index(row, subgroup_id)] = gate_total;
++            partial_up[partial_index(row, subgroup_id)] = up_total;
++        }
++    }
++
++    workgroupBarrier();
++
++    for (
++        var row = subgroup_id;
++        row < OUTPUTS_PER_WG && row_base + row < params.m;
++        row += num_subgroups
++    ) {
++        var gate_row_acc = 0.0;
++        var up_row_acc = 0.0;
++        for (var subgroup = subgroup_invocation_id; subgroup < num_subgroups; subgroup += subgroup_size) {
++            gate_row_acc += partial_gate[partial_index(row, subgroup)];
++            up_row_acc += partial_up[partial_index(row, subgroup)];
++        }
++        let gate = subgroupAdd(gate_row_acc);
++        let up = subgroupAdd(up_row_acc);
++        if (subgroup_invocation_id == 0u) {
++            dst[params.offset_dst + row_base + row] = swiglu(gate, up);
++        }
++    }
++}

vendor/wllama-bonsai/SOURCE.json

@@ -8,15 +8,23 @@
   "patchSet": {
     "format": "git-diff-binary",
     "path": "vendor/wllama-bonsai/PATCH.diff",
-    "bytes": 22878,
-    "sha256": "0a1d7ac5b0edb62cdb8ff5e5e0005c07b5a2fb7a91059f678c49d973fa456949",
+    "bytes": 97836,
+    "sha256": "60a5280bea5fc5ae11065f4c83fd2d532526cdcb7977cbe927cac7ae90926bde",
     "files": [
       "CMakeLists.txt",
       "cpp/glue.hpp",
       "cpp/wllama-context.h",
       "llama.cpp/ggml/src/ggml-webgpu/CMakeLists.txt",
       "llama.cpp/ggml/src/ggml-webgpu/ggml-webgpu-shader-lib.hpp",
+      "llama.cpp/ggml/src/ggml-webgpu/ggml-webgpu.cpp",
+      "llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/common_decls.tmpl",
+      "llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/gated_delta_net.wgsl",
+      "llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/get_rows.wgsl",
+      "llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_decls.tmpl",
+      "llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_vec.wgsl",
       "llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_vec_acc.tmpl",
+      "llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_vec_q1_dual_glu.wgsl",
+      "llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_vec_q1_glu.wgsl",
       "package.json",
       "scripts/docker-compose.yml",
       "src/glue/messages.ts",
@@ -44,8 +52,8 @@
   "files": [
     {
       "path": "vendor/wllama-bonsai/esm/index.js",
-      "bytes": 365536,
-      "sha256": "9a3d15ca29924927b6e95971c5b355f54b581f74089bd74975a063416f7fc65d"
+      "bytes": 366883,
+      "sha256": "a02f02b86d7dac8e3bd6c21225910c755bb85ec64ac7d323bfbfa2477b225e3d"
     },
     {
       "path": "vendor/wllama-bonsai/esm/index.cjs",
@@ -64,13 +72,13 @@
     },
     {
       "path": "public/wasm/wllama.wasm",
-      "bytes": 8026640,
-      "sha256": "93ebb472969c4ccfe9d406e2ebd043da288a6d513a180a57827211278f4c82ed"
+      "bytes": 8048681,
+      "sha256": "dbded8334921b51877ade32009bcaee5b7423e3b39679089a970062c57128acb"
     },
     {
       "path": "public/wasm/wllama-compat.wasm",
-      "bytes": 14872378,
-      "sha256": "91f1566e3dfa4cd591d902c03bfc2c92c9ceef8d5ce93cecae197ee1f4f5dd5d"
+      "bytes": 14885188,
+      "sha256": "f9b1bd2bd2a62a6b95ab52ecda92d10c709bc1ac3b6be9bb450e5adeff8dd39f"
     },
     {
       "path": "public/wasm/wllama-compat.js",
@@ -78,20 +86,27 @@
       "sha256": "0b667db536815fb9e5dc9ffbfbd6ba29affdda38a4ba892d5372ddb41ce9a8c8"
     }
   ],
-  "optimization": {
-    "id": "q1-raw-vec4-wg64",
-    "status": "release",
+  "experiment": {
+    "id": "turbo-q1-tritile-gdn-dual-glu-release-v1",
+    "status": "release-candidate",
     "compileDefinitions": {
       "GGML_WEBGPU_Q1_VEC4": true,
-      "GGML_WEBGPU_Q1_VEC4_WG_SIZE": 64
+      "GGML_WEBGPU_Q1_VEC4_WG_SIZE": 64,
+      "GGML_WEBGPU_Q1_VEC4_OUTPUTS_PER_WG": 4,
+      "GGML_WEBGPU_Q1_VEC4_SHAPE_AWARE_OUTPUTS": true,
+      "GGML_WEBGPU_Q1_VEC4_LARGE_K_THRESHOLD": 4096,
+      "GGML_WEBGPU_Q1_ROW_RETILE": false,
+      "GGML_WEBGPU_COMMAND_SUBMIT_BATCH_SIZE": 64,
+      "GGML_WEBGPU_TURBO_TRACE": false,
+      "GGML_WEBGPU_GPU_PROFILE": false,
+      "GGML_WEBGPU_BATCH_PARAM_UPLOADS": false,
+      "GGML_WEBGPU_Q1_GLU_FUSION": false,
+      "GGML_WEBGPU_Q1_DUAL_GLU_FUSION": true,
+      "GGML_WEBGPU_GDN_CACHE_FUSION": true
     },
-    "wasmSha256": "93ebb472969c4ccfe9d406e2ebd043da288a6d513a180a57827211278f4c82ed",
-    "compatWasmSha256": "91f1566e3dfa4cd591d902c03bfc2c92c9ceef8d5ce93cecae197ee1f4f5dd5d",
-    "evidence": [
-      "results/phase5/q1-raw-vec4-wg64-chrome-ab.json",
-      "results/phase5/q1-raw-vec4-wg64-27b-ab.json",
-      "results/space-agent/27b-html-artifact-q1-raw-vec4-wg64.json",
-      "results/phase5/safari-gate-q1-raw-vec4-wg64-candidate-r6-1_7b.json"
-    ]
+    "wasmSha256": "dbded8334921b51877ade32009bcaee5b7423e3b39679089a970062c57128acb",
+    "moduleSha256": "a02f02b86d7dac8e3bd6c21225910c755bb85ec64ac7d323bfbfa2477b225e3d",
+    "compatWasmSha256": "f9b1bd2bd2a62a6b95ab52ecda92d10c709bc1ac3b6be9bb450e5adeff8dd39f",
+    "compatWorkerSha256": "0b667db536815fb9e5dc9ffbfbd6ba29affdda38a4ba892d5372ddb41ce9a8c8"
   }
 }