Awq Quantization
원문: awq-quantization
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 multi
무엇을 하나요
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: Need maximum ecosystem compatibility (more tools support GPTQ) Working with ExLlamaV2 backend specifically Have older GPUs without Marlin support Use bitsandbytes instead when: Need zero calibration overhead (quantize …
실행 시 본인 API 키(BYOK)로 동작하며, 모델 비용은 사용자 계정에서 직접 결제됩니다.