awq-quantization

name	awq-quantization
description	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.
version	1.0.0
author	Orchestra Research
license	MIT
tags	["Optimization","AWQ","Quantization","4-Bit","Activation-Aware","Memory Optimization","Fast Inference","vLLM Integration","Marlin Kernels"]
dependencies	["autoawq","transformers>=4.45.0","torch>=2.0.0"]

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