| --- |
| pretty_name: KV Cache Quantization Run Commands |
| tags: |
| - vllm |
| - kv-cache |
| - quantization |
| --- |
| |
| # KV cache quantization: run commands |
|
|
| These are the scripts and commands for the post "KV Cache Quantization, Measured: What FP8 and NVFP4 Really Cost". |
| This repo holds commands only. It does not hold model weights, benchmark data, or model responses. |
|
|
| ## Setup |
|
|
| | Item | Value | |
| |---|---| |
| | Models | `Qwen/Qwen3.8-27B`, `google/gemma-4-31B-it` (weights in BF16) | |
| | GPU | NVIDIA H200 141 GB, one GPU per config. A 2-GPU VM ran two configs at the same time. | |
| | vLLM | `0.32.0rc1.dev30+g3ad096faf` (`scripts/bench/setup_vllm.sh`) | |
| | Python packages | `requirements-freeze.txt` | |
| | Terminal-Bench | Terminal-Bench 2.1 with Harbor and the `terminus-2` agent | |
|
|
| ## Configs |
|
|
| | Name | `--kv-cache-dtype` | `--attention-backend` | |
| |---|---|---| |
| | BF16 | `bfloat16` | `FLASH_ATTN` | |
| | BF16-FI | `bfloat16` | `FLASHINFER` | |
| | FP8 | `fp8_e4m3` | `FLASH_ATTN` | |
| | FP4 (NVFP4) | `nvfp4` | `FLASHINFER` | |
| | BF16-TFA | `bfloat16` | `TRITON_FLASH_ATTN` | |
|
|
| For RULER, DocVQA and MRCR, Gemma FP8 used `TRITON_FLASH_ATTN`, and Gemma BF16-FI and FP4 used `--language-model-only` (text only), because FlashInfer has no Gemma image attention on H100 or H200. All Gemma Terminal-Bench runs used `--language-model-only` and the backend in the table above. |
|
|
| Serve command used by `scripts/bench/exp2.sh`: |
|
|
| ```bash |
| vllm serve $MODEL --served-model-name $NAME --port $PORT --dtype bfloat16 \ |
| --max-model-len $MAXLEN --gpu-memory-utilization 0.90 --reasoning-parser $RP --seed 0 \ |
| --kv-cache-dtype $KV --attention-backend $BE |
| ``` |
|
|
| ## Order of work |
|
|
| 1. Install and download: `setup_vllm.sh`, `download_model.sh`. |
| 2. Prepare data: `prep_data.py`, `make_6t.py`, `mrcr.py prep`, `prep_gemma.sh`. |
| 3. RULER, DocVQA, MRCR: `exp2.sh`, driven by `exp2_gpu0.sh`, `exp2_gpu1.sh`, `after_tb.sh`, `gemma_rest.sh`. Score with `score_exp2.py` and `mrcr.py`. |
| 4. Terminal-Bench: `tb_queue.sh`, `tb_server.sh`, `tb_rerun_timeouts.sh`, `gemma_tb.sh`, `gemma_env.sh`. Score with `score_tb.py` and `score_nolim.py`. |
| 5. Nightly backend check for Gemma images, 9 Nov 2026: `scripts/nightly_check/`. |
|
|
| `COMMANDS.md` has every `vllm serve` and `vllm bench serve` command written out in full. |
|
|
| `top_level_commands.txt` has the launch commands from the VM shell history. `exp0*` scripts, `kl_probe.py`, and the smoke scripts were exploratory, and the post does not use their results. |
|
|
| ## Run times |
|
|
| Time is the wall clock for the whole benchmark, including interruptions. It is not a speed test. Each config used the same concurrency, but response lengths differ. |
|
|
| | Config | RULER 4K + 32K + 128K | DocVQA | MRCR | Terminal-Bench k=3 | |
| |---|---|---|---|---| |
| | Qwen BF16 | 55 min | 7.2 min | 78 min | 37.3 h | |
| | Qwen BF16-FI | 62 min | 7.3 min | 89 min | 39.9 h | |
| | Qwen FP8 | 62 min | 7.3 min | 90 min | 24.0 h | |
| | Qwen NVFP4 | 93 min | 7.5 min | 131 min | 26.1 h | |
| | Gemma BF16 | 207 min | 10.9 min | 415 min | 34.5 h | |
| | Gemma BF16-FI | 190 min | not supported | 252 min | 29.2 h | |
| | Gemma FP8 | 181 min | 11.7 min | 337 min | 28.4 h | |
| | Gemma NVFP4 | 316 min | not supported | 443 min | 27.1 h | |
|
|
| Qwen no-limit reruns of timed-out Terminal-Bench tries took about 42 to 45 hours per config. Gemma reruns are not finished, so Gemma Terminal-Bench scores are raw. |
|
|
