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Qwen/Qwen3-8B
by fastserve's self-quantization pipeline
(publish/) — so it can be trusted the way a random community AWQ requant
sometimes can't. Two community quants fastserve auto-detected during its own
benchmark run looped garbage tokens instead of answering; every checkpoint
published here passed an accuracy gate against its bf16 baseline first.ultrachat-200k| bf16 baseline | this AWQ checkpoint | |
|---|---|---|
| GSM8K accuracy (n=30) | 0.8333 | 0.8333 |
eagle3:AngelSlim/Qwen3-8B_eagle3) on vLLM.
Memory = weights only (bf16 vs AWQ); vLLM's KV-cache budget is a separate knob.| Original (HF bf16) | vLLM (bf16) | fastserve (AWQ+spec) | |
|---|---|---|---|
| GSM8K acc | 0.875 | 0.875 | 0.875 |
| Decode speed | 37.3 tok/s | 91.4 tok/s | 323.0 tok/s |
| Weights (VRAM) | 15.26 GiB | 15.26 GiB | 5.68 GiB |
1git clone https://github.com/jireh-father/fastserve && cd fastserve && ./install.sh
2
3# serve an OpenAI-compatible API (auto-picks this AWQ checkpoint)
4./fastserve serve Qwen/Qwen3-8B
5
6# or benchmark the speedup vs the naive baseline
7./fastserve bench Qwen/Qwen3-8B --compare-baseline1curl localhost:8000/v1/completions \
2 -d '{"model": "glenic/Qwen3-8B-AWQ", "prompt": "Q: What is 17*4?\nA:", "max_tokens": 64}'1pip install vllm
2python -m vllm.entrypoints.openai.api_server --model glenic/Qwen3-8B-AWQQwen/Qwen3-8B for terms.