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 on-the-fly) Want to fine-tune with QLoRA Prefer simpler integration Quick start Installation
Default (Triton kernels)
pip install autoawq
With optimized CUDA kernels + Flash Attention
pip install autoawq[kernels]
Intel CPU/XPU optimization
pip install autoawq[cpu]
Requirements: Python 3.8+, CUDA 11.8+, Compute Capability 7.5+
Load pre-quantized model from awq import AutoAWQForCausalLM from transformers import AutoTokenizer
model_name = "TheBloke/Mistral-7B-Instruct-v0.2-AWQ"
model = AutoAWQForCausalLM.from_quantized( model_name, fuse_layers=True # Enable fused attention for speed ) tokenizer = AutoTokenizer.from_pretrained(model_name)
Generate
inputs = tokenizer("Explain quantum computing", return_tensors="pt").to("cuda") outputs = model.generate(**inputs, max_new_tokens=200) print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Quantize your own model from awq import AutoAWQForCausalLM from transformers import AutoTokenizer
model_path = "mistralai/Mistral-7B-Instruct-v0.2"
Load model and tokenizer
model = AutoAWQForCausalLM.from_pretrained(model_path) tokenizer = AutoTokenizer.from_pretrained(model_path)
Quantization config
quant_config = { "zero_point": True, # Use zero-point quantization "q_group_size": 128, # Group size (128 recommended) "w_bit": 4, # 4-bit weights "version": "GEMM" # GEMM for batch, GEMV for single-token }
Quantize (uses pileval dataset by default)
model.quantize(tokenizer, quant_config=quant_config)
Save
model.save_quantized("mistral-7b-awq") tokenizer.save_pretrained("mistral-7b-awq")
Timing: ~10-15 min for 7B, ~1 hour for 70B models.
AWQ vs GPTQ vs bitsandbytes Feature AWQ GPTQ bitsandbytes Speedup (4-bit) ~2.5-3x ~2x ~1.5x Accuracy loss <5% ~5-10% ~5-15% Calibration Minimal (128-1K tokens) More extensive None Overfitting risk Low Higher N/A Best for Production inference GPU inference Easy integration vLLM support Native Yes Limited
Key insight: AWQ assumes not all weights are equally important. It protects ~1% of salient weights identified by activation patterns, reducing quantization error without mixed-precision overhead.
Kernel backends GEMM (default, batch inference) quant_config = { "zero_point": True, "q_group_size": 128, "w_bit": 4, "version": "GEMM" # Best for batch sizes > 1 }
GEMV (single-token generation) quant_config = { "version": "GEMV" # 20% faster for batch_size=1 }
Limitation: Only batch size 1, not good for large context.
Marlin (Ampere+ GPUs) from transformers import AwqConfig, AutoModelForCausalLM
config = AwqConfig( bits=4, version="marlin" # 2x faster on A100/H100 )
model = AutoModelForCausalLM.from_pretrained( "TheBloke/Mistral-7B-AWQ", quantization_config=config )
Requirements: Compute Capability 8.0+ (A100, H100, RTX 40xx)
ExLlamaV2 (AMD compatible) config = AwqConfig( bits=4, version="exllama" # Faster prefill, AMD GPU support )
HuggingFace Transformers integration Direct loading from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained( "TheBloke/zephyr-7B-alpha-AWQ", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained("TheBloke/zephyr-7B-alpha-AWQ")
Fused modules (recommended) from transformers import AwqConfig, AutoModelForCausalLM
config = AwqConfig( bits=4, fuse_max_seq_len=512, # Max sequence length for fusing do_fuse=True # Enable fused attention/MLP )
model = AutoModelForCausalLM.from_pretrained( "TheBloke/Mistral-7B-OpenOrca-AWQ", quantization_config=config )
Note: Fused modules cannot combine with FlashAttention2.
vLLM integration from vllm import LLM, SamplingParams
vLLM auto-detects AWQ models
llm = LLM( model="TheBloke/Llama-2-7B-AWQ", quantization="awq", dtype="half" )
sampling = SamplingParams(temperature=0.7, max_tokens=200) outputs = llm.generate(["Explain AI"], sampling)
Performance benchmarks Memory reduction Model FP16 AWQ 4-bit Reduction Mistral 7B 14 GB 5.5 GB 2.5x Llama 2-13B 26 GB 10 GB 2.6x Llama 2-70B 140 GB 35 GB 4x Inference speed (RTX 4090) Model Prefill (tok/s) Decode (tok/s) Memory Mistral 7B GEMM 3,897 114 5.55 GB TinyLlama 1B GEMV 5,179 431 2.10 GB Llama 2-13B GEMM 2,279 74 10.28 GB Accuracy (perplexity) Model FP16 AWQ 4-bit Degradation Llama 3 8B 8.20 8.48 +3.4% Mistral 7B 5.25 5.42 +3.2% Qwen2 72B 4.85 4.95 +2.1% Custom calibration data
Use custom dataset for domain-specific models
model.quantize( tokenizer, quant_config=quant_config, calib_data="wikitext", # Or custom list of strings max_calib_samples=256, # More samples = better accuracy max_calib_seq_len=512 # Sequence length )
Or provide your own samples
calib_samples = [ "Your domain-specific text here...", "More examples from your use case...", ] model.quantize(tokenizer, quant_config=quant_config, calib_data=calib_samples)
Multi-GPU deployment model = AutoAWQForCausalLM.from_quantized( "TheBloke/Llama-2-70B-AWQ", device_map="auto", # Auto-split across GPUs max_memory={0: "40GB", 1: "40GB"} )
Supported models
35+ architectures including:
Llama family: Llama 2/3, Code Llama, Mistral, Mixtral Qwen: Qwen, Qwen2, Qwen2.5-VL Others: Falcon, MPT, Phi, Yi, DeepSeek, Gemma Multimodal: LLaVA, LLaVA-Next, Qwen2-VL Common issues
CUDA OOM during quantization:
Reduce batch size
model.quantize(tokenizer, quant_config=quant_config, max_calib_samples=64)
Slow inference:
Enable fused layers
model = AutoAWQForCausalLM.from_quantized(model_name, fuse_layers=True)
AMD GPU support:
Use ExLlama backend
config = AwqConfig(bits=4, version="exllama")
Deprecation notice
AutoAWQ is officially deprecated. For new projects, consider:
vLLM llm-compressor: https://github.com/vllm-project/llm-compressor MLX-LM: For Mac devices with Apple Silicon
Existing quantized models remain usable.
References Paper: AWQ: Activation-aware Weight Quantization (arXiv:2306.00978) - MLSys 2024 Best Paper GitHub: https://github.com/casper-hansen/AutoAWQ MIT Han Lab: https://github.com/mit-han-lab/llm-awq Models: https://huggingface.co/models?library=awq