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LLM Inference Serving

Architecture Patterns

Summary

LLM inference serving systems are the infrastructure stack that makes large language models available in production. These systems handle model loading, request batching, KV-cache management, speculative decoding, and continuous batching to maximize throughput while minimizing latency for end users.

Key Characteristics

  • Continuous Batching: Dynamically add and remove sequences from batches as they complete, maximizing GPU utilization
  • KV-Cache Management: Efficient memory management for the key-value cache across concurrent requests
  • Speculative Decoding: Use a smaller draft model to propose tokens that the large model verifies in parallel
  • Quantization: Reduce model precision (FP16 to INT4/INT8) for faster inference with minimal quality loss

Popular Models

  • vLLM: High-throughput serving engine with PagedAttention for efficient KV-cache management
  • TensorRT-LLM: NVIDIA's optimized inference framework with graph compilation and kernel fusion
  • TGI (Text Generation Inference): HuggingFace's production-grade inference server
  • llama.cpp: CPU-first inference engine with GPU acceleration, focused on local deployment

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Explain LLM inference serving systems. Cover: key techniques - continuous batching (process multiple requests simultaneously), paged attention (manage KV-cache like virtual memory), prefix caching (cache common prompt prefixes), speculative decoding (use a small model to draft, large model to verify). Impact: 2-10x throughput improvements, significant cost reduction. Popular systems: vLLM, TGI, TensorRT-LLM, ONNX Runtime. Buyer considerations: open source vs managed, GPU memory requirements, latency vs throughput trade-offs.