Efficient LLM Inference (2023)

2024-01-04Hacker NewsShare on TwitterShare on LinkedinCopy link

Summary:
  • The article discusses three main approaches to serving a given LLM more efficiently: quantization, distillation, and optimization.
  • Optimization is the first step to reduce overhead in code and improve performance.
  • Distillation is generally more effective than quantization in reducing model size while maintaining performance, but it can be costly.
  • Quantization is a cheaper option that reduces precision but sacrifices some accuracy for improved performance.
  • The tradeoff between distillation and quantization depends on the available resources and cost considerations.
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