NVIDIA / NVIDIA/TensorRT-LLM

[Call for contributions] Feature parity between TensorRT and PyTorch backend - Quantization

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#3,701 13 comments 0 reactions 1 assignee View on GitHub

@juney-nvidia is already working on this.

Since Apr 20, 2025.

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Description

  • NVFP4
  • FP8 per tensor
  • FP8 block scaling(DeepSeek recipe)
  • FP8 rowwise
  • SmoothQuant
  • W8A16 weight only
  • W4A8 weight only
  • W4A16 GPTQ
  • W4A8 GPTQ
  • W4A8 Qserve
  • W4A8 AWQ @qsang-nv
  • W4A16 AWQ @danielafrimi
  • INT8
  • FP8 KV Cache
  • INT8 KV Cache

Contributor guide

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First steps

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  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

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