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Serving Large Language Models at Scale: A Comprehensive Review

Introduction

In this post, I'll share my analysis of different approaches to serving large language models efficiently at scale. I'll cover key papers in this space and share insights from both research and practical perspectives.

Key Papers

vLLM: GPU-Efficient LLM Serving with Page-Based Attention

Key Insights: - Novel paged attention mechanism for efficient memory management - Significant throughput improvements over traditional approaches - Trade-offs between memory efficiency and computational overhead

My Thoughts: The paged attention approach is particularly elegant... [continue analysis]

System Design Considerations

When building LLM serving systems, there are several key aspects to consider:

  1. Memory Management
  2. Request Scheduling
  3. Load Balancing
  4. Batching Strategies

[Continue with detailed analysis...]

Future Directions

Based on current trends, here are some promising research directions:

  1. Hybrid serving architectures
  2. Dynamic resource allocation
  3. Specialized hardware acceleration

References

  1. Paper 1
  2. Paper 2 [...]