LLM Inference: A Survey

on Nlp, Survey, Llm, Inference, Optimization, Deployment

Introduction

This post explores the survey on Large Language Model inference, covering optimization techniques, deployment strategies, and performance considerations for running LLMs in production environments.

Key Topics Covered

  • Inference Optimization Techniques
  • Model Quantization and Compression
  • Hardware Acceleration (GPU, TPU, specialized chips)
  • Distributed Inference Strategies
  • Caching and Memory Management
  • Latency and Throughput Optimization
  • Edge Deployment Considerations
  • Cost-Performance Trade-offs
  • Real-world Deployment Challenges

Summary

[Add your summary and insights from the survey paper here]

References

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