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AWQ
4-bit quantization for LLMs with hardware-efficient inference
MIT
ABOUT
Running large language models requires expensive GPUs with high memory capacity — a 70B parameter model needs over 140GB in FP16, putting it out of reach for most teams and deployments. AWQ (Activation-aware Weight Quantization) compresses LLM weights to 4-bit integers while preserving accuracy, reducing memory footprint by 3-4x. Unlike generic quantization, AWQ protects the small fraction of weights that are critical for model quality by analyzing activation patterns.
INSTALL
pip install awqINTEGRATION GUIDE
1. Deploy 70B parameter models on a single GPU using 4-bit quantization without accuracy loss
2. Run LLM inference on consumer GPUs with lower memory requirements for local development
3. Quantize models for edge deployment where memory and power are constrained
4. Integrate with vLLM and TGI serving frameworks for production-quantized inference
5. Reduce inference costs by fitting larger models on fewer GPUs in cloud deployments
TAGS
pythonquantizationllminference-optimizationmodel-compressiongpu