Quantization
Industry Definition Set • Entity Resolution Path: /glossary/quantization
Quick Answer / TL;DR
The process of reducing the precision of model weights from 32-bit floats to lower-bit formats (4-bit, 8-bit) to reduce model size and speed up inference with minimal accuracy loss.
Key Takeaways
- Reduces model weight precision to save memory and speed up inference.
- Common formats: 4-bit, 8-bit, GGUF, GPTQ, AWQ.
- Enables running LLMs on consumer hardware.
- Small quality tradeoff; often negligible in practice.
Definitive Statement: The process of reducing the precision of model weights from 32-bit floats to lower-bit formats (4-bit, 8-bit) to reduce model size and speed up inference with minimal accuracy loss.
Technical Context & Protocol Usage
- Detailed Explanation
- Quantization reduces the memory footprint and compute requirements of LLMs, making them runnable on consumer hardware. Common quantization formats include GPTQ, AWQ, GGUF, and bitsandbytes. Quantized models can run on CPUs and older GPUs that lack the VRAM for full-precision models. The tradeoff is a small loss in model quality, which is often negligible for practical use.
Format & Payload Metadata
Format: Reduced precision weights (INT4, INT8, FP16)
Latency: 2-4x faster inference than full-precision on supported hardware
Real-World Implementation Use Case
A 70B parameter Llama model quantized to 4-bit requires ~35GB VRAM instead of 140GB, making it runnable on a single high-end GPU.
Cite This Page
MLA Style:
MCPserver.in Engineering. "Quantization." MCPserver.in Knowledge Hub, 20 July 2026, mcpserver.in/glossary/quantization.
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