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vllm-omni/.claude/skills/quantization/references/modality-compat.md
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2026-06-11 06:41:31 +08:00

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Quantization Compatibility by Modality

Use this as a routing guide. The source of truth is the local method docs under docs/user_guide/quantization/ and the current model implementation.

Summary

  • AR and omni language-model quantization mostly follows upstream vllm, with vLLM-Omni scoping for multi-stage models.
  • Diffusion quantization is method-specific and model-specific. It usually targets the diffusion transformer only.
  • Pre-quantized checkpoints should quantize only the component that has quantized weights and scale tensors.

Model Type Matrix

Model Type Typical Quantization Scope Notes
AR text models Language model Upstream vllm methods usually apply
Omni/TTS models Thinker or AR language-model stage Audio, vision, talker, and codec stages usually stay BF16
DiT image models Diffusion transformer Check FP8, Int8, GGUF, ModelOpt, AutoRound docs
DiT video models Diffusion transformer HunyuanVideo FP8 and Wan2.2 MXFP paths are documented; validate other video paths
Multi-stage diffusion Selected stage only Use per-component routing and per-stage validation
Audio diffusion Usually unsupported Prefer dtype, offload, cache, or parallelism unless docs say otherwise

Method Hints

Goal Candidate
AR memory reduction AWQ, GPTQ, AutoRound, ModelOpt where checkpoint supports it
CUDA diffusion W8A8 FP8 or Int8
Pre-quantized CUDA diffusion checkpoint ModelOpt or GGUF
Ascend NPU diffusion Int8, MXFP8, MXFP4, MXFP4 DualScale, msModelSlim
Intel XPU checkpoint AutoRound/INC paths
Conservative image quality selective FP8/Int8 with ignored_layers
Unsupported diffusion model cache acceleration, CPU/layerwise offload, dtype, TP/SP/CFG parallelism

Validation Rule

Compatibility means more than successful loading. A method is compatible only after it has:

  • correct component scope
  • complete checkpoint/config metadata if offline
  • fixed-seed quality comparison against BF16
  • acceptable latency and memory behavior
  • docs or examples that state model, method, hardware, and caveats