Individual skill
optimization-awq
Activation-aware weight quantization for 4-bit LLM compression with 3x speedup and minimal accuracy loss. Use when deploying large models (7B-70B) on limited GPU memory, when you need faster inference than GPTQ with better accuracy preservation, or for instruction-tuned and multimodal models. MLSys 2024 Best Paper Award winner.
cli-tool/components/skills/ai-research/optimization-awq/SKILL.md
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- anthropic Repository topic
- anthropic-claude Repository topic
- claude Repository topic
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- optimization Declared skill tag
- awq Declared skill tag
- quantization Declared skill tag
- 4-bit Declared skill tag
- activation-aware Declared skill tag
- memory-optimization Declared skill tag
- fast-inference Declared skill tag
- vllm-integration Declared skill tag
- marlin-kernels Declared skill tag
- research Derived category
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# AWQ (Activation-aware Weight Quantization)
4-bit quantization that preserves salient weights based on activation patterns, achieving 3x speedup with minimal accuracy loss.
## When to use AWQ
**Use AWQ when:**
- Need 4-bit quantization with <5% accuracy loss
- Deploying instruction-tuned or chat models (AWQ generalizes better)
- Want ~2.5-3x inference speedup over FP16
- Using vLLM for production serving
- Have Ampere+ GPUs (A100, H100, RTX 40xx) for Marlin kernel support
**Use GPTQ instead when:**