/optimization-awq

name: optimization-awq
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.
license: MIT
metadata:
  version: 1.0.0
  author: Orchestra Research
  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 (RT