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README.md

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+---
+license: mit
+task_categories:
+  - text-generation
+tags:
+  - benchmark
+  - inference
+  - llm
+  - nvidia
+  - rtx-5090
+  - llama-cpp
+  - vllm
+  - speed
+  - moe
+size_categories:
+  - n<1K
+configs:
+  - config_name: default
+    data_files:
+      - split: train
+        path: benchmarks.csv
+---
+
+# RTX 5090 LLM Benchmarks
+
+Speed benchmarks for quantized LLMs on NVIDIA RTX 5090 32GB, measured with [llm-bench-rig](https://github.com/notwitcheer/llm-bench-rig).
+
+## What's measured
+
+- **Prompt processing (pp)**: parallel batched token throughput at context lengths 128, 512, 2048, 4096, 8192, 16384
+- **Text generation (tg)**: sequential autoregressive token throughput at 128 tokens
+- All models fully GPU-offloaded (ngl=99)
+
+## Hardware
+
+| Component | Spec |
+|---|---|
+| GPU | NVIDIA GeForce RTX 5090 32GB (Blackwell, sm_120a) |
+| CPU | AMD Ryzen 5 9600 (6c/12t) |
+| RAM | 64GB DDR5-5600 |
+| OS | Ubuntu 26.04 LTS |
+| CUDA | 12.8 (patched for glibc 2.41) |
+
+## Schema
+
+| Column | Description |
+|---|---|
+| `model` | Model name |
+| `architecture` | Dense or MoE (with active param count) |
+| `params_b` | Total parameters in billions |
+| `quant` | Quantization method |
+| `size_gib` | File size in GiB |
+| `engine` | Inference engine (llama.cpp or vLLM) |
+| `backend` | Compute backend (CUDA) |
+| `gpu` | GPU model |
+| `vram_gb` | VRAM in GB |
+| `test` | Benchmark test (pp128, pp512, ..., tg128) |
+| `tokens_per_sec` | Throughput in tokens/second |
+| `stddev` | Standard deviation |
+| `date` | Benchmark date |
+
+## Key findings
+
+MoE (3B active) vs Dense (27B) on same-family Qwen3.6 models:
+- Prompt processing: **2.4x faster** across all context lengths
+- Text generation: **3.5x faster** (271 vs 77 t/s)
+- Both degrade ~17% at 16K context (attention + VRAM, not parameter count)
+
+## Tooling
+
+Benchmarks generated with [llm-bench-rig](https://github.com/notwitcheer/llm-bench-rig) — open-source benchmark pipeline for GGUF and safetensors models.

benchmarks.csv

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+model,architecture,params_b,quant,size_gib,engine,backend,gpu,vram_gb,test,tokens_per_sec,stddev,date
+Qwen3.6-35B-A3B,MoE (3B active),34.66,UD-Q4_K_M,20.61,llama.cpp,CUDA,RTX 5090,32,pp128,3605.03,48.69,2026-05-28
+Qwen3.6-35B-A3B,MoE (3B active),34.66,UD-Q4_K_M,20.61,llama.cpp,CUDA,RTX 5090,32,pp512,9239.86,63.85,2026-05-28
+Qwen3.6-35B-A3B,MoE (3B active),34.66,UD-Q4_K_M,20.61,llama.cpp,CUDA,RTX 5090,32,pp2048,9041.04,65.96,2026-05-28
+Qwen3.6-35B-A3B,MoE (3B active),34.66,UD-Q4_K_M,20.61,llama.cpp,CUDA,RTX 5090,32,pp4096,8760.53,53.07,2026-05-28
+Qwen3.6-35B-A3B,MoE (3B active),34.66,UD-Q4_K_M,20.61,llama.cpp,CUDA,RTX 5090,32,pp8192,8442.99,37.16,2026-05-28
+Qwen3.6-35B-A3B,MoE (3B active),34.66,UD-Q4_K_M,20.61,llama.cpp,CUDA,RTX 5090,32,pp16384,7713.79,15.46,2026-05-28
+Qwen3.6-35B-A3B,MoE (3B active),34.66,UD-Q4_K_M,20.61,llama.cpp,CUDA,RTX 5090,32,tg128,270.97,1.24,2026-05-28
+Qwen3.6-27B,Dense,26.90,Q4_K_M,15.66,llama.cpp,CUDA,RTX 5090,32,pp128,2972.93,322.84,2026-05-28
+Qwen3.6-27B,Dense,26.90,Q4_K_M,15.66,llama.cpp,CUDA,RTX 5090,32,pp512,3825.83,41.56,2026-05-28
+Qwen3.6-27B,Dense,26.90,Q4_K_M,15.66,llama.cpp,CUDA,RTX 5090,32,pp2048,3740.84,1.29,2026-05-28
+Qwen3.6-27B,Dense,26.90,Q4_K_M,15.66,llama.cpp,CUDA,RTX 5090,32,pp4096,3644.93,2.76,2026-05-28
+Qwen3.6-27B,Dense,26.90,Q4_K_M,15.66,llama.cpp,CUDA,RTX 5090,32,pp8192,3484.57,7.20,2026-05-28
+Qwen3.6-27B,Dense,26.90,Q4_K_M,15.66,llama.cpp,CUDA,RTX 5090,32,pp16384,3161.79,3.66,2026-05-28
+Qwen3.6-27B,Dense,26.90,Q4_K_M,15.66,llama.cpp,CUDA,RTX 5090,32,tg128,77.09,0.16,2026-05-28