3 Ways to Optimize LLM Inference
Discover three essential techniques to speed up Large Language Model inference and reduce costs.
Hammad Qaiser
Series: Building AI Systems
- 1How to Build a RAG System From Scratch
- 23 Ways to Optimize LLM Inference(Current)
- 3Deploying LLMs with vLLM and Ray
Running large language models in production can be expensive and slow. Here are three ways to optimize inference.
1. Quantization
Quantization reduces the precision of the model's weights (e.g., from 16-bit float to 8-bit or 4-bit integer). This drastically reduces memory usage and speeds up memory-bound inference tasks with minimal impact on quality. Techniques like AWQ or GPTQ are popular.
2. KV Cache Optimization
The Key-Value (KV) cache stores past attention states to avoid recomputing them. Optimizing KV cache through techniques like PagedAttention (used in vLLM) prevents memory fragmentation and allows larger batch sizes.
3. Speculative Decoding
Speculative decoding uses a smaller, faster "draft" model to predict the next few tokens. The larger "target" model then verifies these tokens in parallel. If the draft model is accurate, this can double or triple the generation speed.
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