AI BriefingOptimizing CPU LLM Inference with SmoothQuant
Key point
This covers how to use SmoothQuant technology to reduce LLM size and improve inference efficiency on Intel CPUs.
Details
LLMs incur high computing costs and GPU dependency due to their massive parameters, which becomes a cost burden for many organizations. To address this, Quantization technology, which reduces model size and inference latency, plays a key role.
Typical quantization converts model parameters from 16-bit floating point to 8-bit integers (INT8) and so on, reducing memory usage and increasing computation speed. However, LLMs have a characteristic where very large values, called Outliers, occur in certain activation channels, causing a significant drop in model accuracy with conventional methods.
SmoothQuant is a technique that mitigates the impact of outliers by applying a mathematical transformation to weights and activation values. This enables 8-bit quantization without degrading model quality, and as a result, efficient LLM inference becomes possible even in Intel Xeon CPU environments.
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