Add measured performance section (M4 Max)

README.md

@@ -1,32 +1,56 @@
----
-license: apache-2.0
-base_model: HuggingFaceTB/SmolLM2-360M-Instruct
-pipeline_tag: text-generation
-library_name: litert-lm
-tags:
-- chat
-- litert-lm
-- smollm
-- on-device
----
-
-# litert-community/SmolLM2-360M-Instruct
-
-This model provides a variant of
-[HuggingFaceTB/SmolLM2-360M-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-360M-Instruct) that is ready for
-deployment on Android using the
-[LiteRT-LM](https://github.com/google-ai-edge/LiteRT-LM).
-
-## Use the model
-
-### Android
-
-#### Edge Gallery App
-* Download or build the [app](https://github.com/google-ai-edge/gallery?tab=readme-ov-file#-get-started-in-minutes) from GitHub.
-
-* Install the [app](https://play.google.com/store/apps/details?id=com.google.ai.edge.gallery&pli=1) from Google Play.
-
-* Follow the instructions in the app.
-
-To build the demo app from source, please follow the [instructions](https://github.com/google-ai-edge/gallery/blob/main/README.md)
-from the GitHub repository.
+---
+license: apache-2.0
+base_model: HuggingFaceTB/SmolLM2-360M-Instruct
+pipeline_tag: text-generation
+library_name: litert-lm
+tags:
+- chat
+- litert-lm
+- smollm
+- on-device
+---
+
+# litert-community/SmolLM2-360M-Instruct
+
+This model provides a variant of
+[HuggingFaceTB/SmolLM2-360M-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-360M-Instruct) that is ready for
+deployment on Android using the
+[LiteRT-LM](https://github.com/google-ai-edge/LiteRT-LM).
+
+## Use the model
+
+### Android
+
+#### Edge Gallery App
+* Download or build the [app](https://github.com/google-ai-edge/gallery?tab=readme-ov-file#-get-started-in-minutes) from GitHub.
+
+* Install the [app](https://play.google.com/store/apps/details?id=com.google.ai.edge.gallery&pli=1) from Google Play.
+
+* Follow the instructions in the app.
+
+To build the demo app from source, please follow the [instructions](https://github.com/google-ai-edge/gallery/blob/main/README.md)
+from the GitHub repository.
+
+## Performance (measured)
+
+### Apple M4 Max
+
+Measured with the LiteRT-LM CLI: `litert-lm benchmark -p 256 -d 256 --runs 3 --cache no`
+(litert-lm 0.15.0) on an idle Apple M4 Max (macOS); 256 prefill / 256 decode tokens, 3 iterations
+averaged by the tool. A desktop reference point — phone-side figures vary by SoC and backend.
+
+| Backend | Prefill (tokens/s) | Decode (tokens/s) | Time-to-first-token (s) |
+|---|---|---|---|
+| CPU | 588 | 37.3 | 0.63 |
+| GPU | 2,773 | 133.6 | 0.11 |
+
+### Galaxy S26 — GPU vs CPU (litert-lm 0.16.0)
+
+Measured on a physical Samsung Galaxy S26 (SM-S942Q, Snapdragon 8 Elite Gen 5 / SM8850, Android 16) with `litert_lm_advanced_main` from the litert-lm v0.16.0 release; the GPU backend is OpenCL (`LITERT_CL`). One fixed 205-token prompt text (206 tokens under this tokenizer), `--benchmark`. GPU: two runs back-to-back; CPU: three runs spread across device thermal states (skin temperature up to 41.9 °C recorded on the warmest) — small-model CPU speed on a phone moves a lot with temperature, and the CPU range below is that, not noise. Peak RSS is the process VmHWM. Before quoting, the same file was run on each backend with a real prompt: both produced a correct text answer.
+
+| Backend | Prefill (206 tok) | Decode | Time-to-first-token | Peak RSS |
+|---|---|---|---|---|
+| **GPU (OpenCL)** | **540–541 tok/s** | **31.5–36.0 tok/s** | **0.41 s** | **546 MB** |
+| CPU (XNNPACK) | 118–211 tok/s | 12.7–19.5 tok/s | 1.03–1.82 s | 1409 MB |
+
+The GPU takes the whole graph (decode 1362/1362, prefill 1491/1491 ops on `LITERT_CL`) and wins across the board on this bundle: prefill ≥2.6× at any temperature, decode 1.6–2.8×, time-to-first-token 2.5–4.4×, and peak RSS 2.6× lower.