vllm-quantpack

Experimental, early stage. A vLLM general plugin that serves EXL3 (exllamav3 trellis) checkpoints, and the model-family fixes they need, in stock vLLM: no patch to vLLM, no per-model plugin.

What it does

• Registers quant_method: "exl3", so vllm serve <repo> loads EXL3 packs with no extra flag:
  • linear layers at any per-tensor bitrate, including fused layers whose shards have different bitrates;
  • lm_head as an EXL3 tensor (head_bits) or dense;
  • embed_tokens as int8 rows with a per-row scale, dequantized on the gathered rows;
  • routed MoE experts on exllamav3's fused kernel, capturable in CUDA graphs;
  • tensor parallelism for dense layers (-tp N) and expert parallelism for MoE (-tp N --enable-expert-parallel).
• Model-family fixes:
  • Qwen 3.5 / 3.6 / 3.8: the vision tower's duplicated attention tensors;
  • Qwen3.8-Flash-Next: the FP8 PLE lookup on GPUs below sm_89.

Requirements

• vLLM 0.31 (0.30 also works).
• exllamav3 1.6.5, as the prebuilt wheel that matches your torch and CUDA. The prebuilt wheels are on the exllamav3 releases page; PyPI only has a generic wheel that compiles the CUDA kernels on first use (it needs a CUDA toolkit and takes a while). For vLLM 0.31 (torch 2.13.0, CUDA 13):

  W=exllamav3-1.6.5+cu132.torch2.13.0-cp312-cp312-linux_x86_64.whl
  curl -L -o /tmp/$W "https://github.com/turboderp-org/exllamav3/releases/download/v1.6.5/${W/+/%2B}"
  pip install /tmp/$W
  

  For other versions: python -c "import torch; print(torch.__version__, torch.version.cuda)" and pick the matching asset.
• An NVIDIA GPU of compute capability 8.0 or newer (Ampere, Ada, Hopper, Blackwell).
• FlashInfer's prebuilt kernels in the same environment, unless you have a CUDA toolkit: without them vLLM looks for nvcc at start and fails with Could not find nvcc and default cuda_home='/usr/local/cuda' doesn't exist. For vLLM 0.31:

  pip install flashinfer-cubin==0.7.0.post1 flashinfer-jit-cache==0.7.0.post1+cu130 \
    --extra-index-url https://flashinfer.ai/whl/ --extra-index-url https://flashinfer.ai/whl/cu130/
  

• The Python headers and a C compiler (sudo apt-get install python3.12-dev build-essential on Debian/Ubuntu). Triton compiles a small C helper every time vLLM starts; with a system Python and no headers it stops with fatal error: Python.h: No such file or directory.

Each model card on Hugging Face has the full install block that was tested for that checkpoint.

Install

pip install vllm-quantpack

It replaces OrcaSAQ2, qwen38_exl3_vision, flint_vllm and spark_vllm. If any of them is installed in the same environment, uninstall it, or load only this plugin with VLLM_PLUGINS=quantpack.

Status

Credits

Includes code from OrcaSAQ2 (Apache-2.0). The EXL3 format and kernels are exllamav3 by turboderp (MIT), a streamlined variant of QTIP. See NOTICE.

License

Apache-2.0.