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Model Quantization: Making LLMs Smaller and Faster

Model quantization reduces the precision of neural network weights, making models smaller and faster with minimal accuracy loss. This enables running large language models on consumer hardware, edge devices, and cost-effective inference servers.

Quantization Fundamentals

Models are typically trained in FP32 (32-bit floating point) or BF16 (16-bit bfloat). Quantization converts weights to lower precision: INT8 (8-bit), INT4 (4-bit), or even 2-bit. Weight size decreases proportionally—INT4 uses 1/8 the memory of FP32.

Quantization introduces quantization error. The trade-off is between compression ratio and accuracy. Most models retain 95-99% of their accuracy at INT4.…