In-depth Analysis of Modern Large Model Inference Infrastructure: From vLLM Core to Production-Grade Deployment Architecture

This article comprehensively analyzes the core technology stack of modern AI inference infrastructure, covering vLLM internal mechanisms, distributed inference, quantization compression, dynamic batching, and production environment deployment practices, providing a systematic guide for building large-scale LLM service systems. As the scale of large language models continues to expand, the inference system architecture directly affects user experience and operational costs. This article will explain from underlying kernel optimization to top-level deployment architecture.

Large model inference faces conflicting goals of low latency, high throughput, and low cost. Traditional inference methods have issues like memory waste. The emergence of vLLM is an important milestone; its PagedAttention technology significantly improves GPU memory utilization and throughput. Understanding vLLM is key to mastering modern inference infrastructure.

vLLM's PagedAttention mechanism draws on virtual memory management, dividing KV cache into fixed blocks to solve memory fragmentation issues, supporting memory sharing and efficient dynamic batching. The scheduler uses a collaborative strategy to flexibly allocate resources during the prefill and decoding phases, maximizing GPU utilization.

When the model exceeds the memory of a single card, distributed inference is an inevitable choice. vLLM supports tensor parallelism (splitting model layers and synchronizing with all-reduce), pipeline parallelism (grouping by layers), and hybrid parallelism; the cutting-edge direction is separating prefill and decoding, assigning the two phases to different GPU clusters to optimize costs.

Model quantization (e.g., FP8) can halve memory and computation; the Hopper architecture natively supports FP8. KV cache compression (quantization, dynamic compression) alleviates memory pressure from context growth. LMCache extends cache management capabilities, supporting cross-request sharing and persistence.

Continuous batching allows new requests to fill the positions of completed requests, keeping the GPU fully loaded; speculative decoding uses a small draft model to generate candidate tokens and then verifies them, improving decoding speed. These strategies effectively balance throughput and latency.

The vLLM Production Stack covers functions such as routing (intelligent request distribution), auto-scaling (dynamically adjusting instances), fault tolerance (failure detection and switching), and LoRA dynamic loading (single model serving multiple fine-tuned versions), addressing production deployment pain points.

Cutting-edge trends include expert parallelism for MoE models, optimization for next-generation AI hardware, and standardization of OpenAI-compatible APIs. Summary: Modern inference infrastructure is complex and requires combining technical principles with toolchains; open-source projects like ai-infra-application provide practical references, and there is huge room for future optimization.