Gptq

GPTQ (Generative Pre-trained Transformer Quantization)

Post-training quantization method that compresses LLMs to 4-bit with minimal accuracy loss using group-wise quantization.

When to use GPTQ

Use GPTQ when:

• Need to fit large models (70B+) on limited GPU memory
• Want 4× memory reduction with <2% accuracy loss
• Deploying on consumer GPUs (RTX 4090, 3090)
• Need faster inference (3-4× speedup vs FP16)

Use AWQ instead when:

• Need slightly better accuracy (<1% loss)
• Have newer GPUs (Ampere, Ada)
• Want Marlin kernel support (2× faster on some GPUs)

Use bitsandbytes instead when:

• Need simple integration with transformers
• Want 8-bit quantization (less compression, better quality)
• Don't need pre-quantized model files

Quick start

Installation

# Install AutoGPTQ
pip install auto-gptq

# With Triton (Linux only, faster)
pip install auto-gptq[triton]

# With CUDA extensions (faster)
pip install auto-gptq --no-build-isolation

# Full installation
pip install auto-gptq transformers accelerate

Load pre-quantized model

from transformers import AutoTokenizer
from auto_gptq import AutoGPTQForCausalLM