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Model Deployment: vLLM & LitServe

Learn deployment requirements, PagedAttention, Continuous Batching, and serve models via vLLM and LitServe.

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▶📚 Prerequisites(1)

🎓 Learning objectives

  • •Explain how PagedAttention eliminates memory fragmentation
  • •Compare static batching and continuous (iteration-level) batching
  • •Deploy models using vLLM and configure LitServe architectures

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📂 Subtopics

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Serving Frameworks: vLLM vs TGI vs Triton (and LitServe)

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API Design for LLMs: Streaming, Async and Timeouts

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Scaling Strategies: Replicas, Load Balancing and Parallelism

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Cost Optimization: Token Budgets, Caching and Model Routing

Related concepts

llm-optimizationllm-observability

Next to learn

llm-observability →
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