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gptq

Quantizes LLMs to 4-bit (also 3-bit) with GPTQ using group-wise quantization (group size 128 by default), via AutoGPTQ and transformers. Covers loading pre-quantized GPTQ models, quantizing your own model, choosing group size, selecting ExLlamaV2, Marlin, or Triton kernels, and QLoRA fine-tuning with PEFT. Use when fitting 70B-class models onto limited or consumer GPUs, cutting memory about 4x versus FP16, speeding up inference, finding pre-quantized checkpoints on HuggingFace, or fine-tuning a quantized model with LoRA. For slightly better accuracy on newer GPUs use AWQ instead, and for simple 8-bit or on-the-fly quantization use bitsandbytes.

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# 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)