Inside vLLM: Anatomy of a High-Throughput LLM Inference System

vLLM is one of the most widely used open-source inference engines for LLMs, and Aleksa Gordić’s recent post provides a solid technical walkthrough of its internals. It’s aimed at developers curious about how modern LLM engines are built, or those considering contributing to vLLM, SGLang, and similar projects. The analysis is pinned to commit 42172ad (August 9th, 2025) and focuses on the new V1 engine (V0 is deprecated).

Core Engine Components

The article starts with the offline synchronous setup, using a simple LLM object. The engine constructor is broken down into several key parts:

• vLLM config – All the knobs for model, cache, parallelism, etc.
• Processor – Converts raw inputs into engine core requests via validation and tokenization.
• Engine Core Client – In the example, it’s an InprocClient that runs in-process; for production you’d move to a multi-process client like DPLBAsyncMPClient.
• Output Processor – Converts engine core outputs to user-facing request outputs.

The engine core itself contains the model executor (driving forward passes), a structured output manager (for guided decoding), and a scheduler with waiting/running queues. The scheduler uses a policy (FCFS or priority) and includes the KV cache manager.

Paged Attention & KV Cache

The KV cache manager is the heart of paged attention. It maintains a free_block_queue of available blocks, often hundreds of thousands depending on VRAM and block size. The block size for a standard transformer (non-MLA) is calculated as:

2 * block_size * num_kv_heads * head_size * dtype_num_bytes

which for default settings yields a practical block size. This paging mechanism allows for efficient memory management and high throughput.

Advanced Features

The post doesn’t stop at the basics. It outlines advanced features that make vLLM production-ready:

• Chunked prefill – Splits long prompts into chunks to interleave with generation.
• Prefix caching – Reuses KV cache for shared prompt prefixes.
• Guided decoding – Constrains output to a schema or grammar.
• Speculative decoding – Uses a draft model to speed up generation.
• Disaggregated P/D – Separates prefill and decode across different GPUs.

It also covers scaling to multi-GPU and multi-node setups, plus the serving layer for handling concurrent web traffic. Benchmarks and auto-tuning are mentioned for measuring latency and throughput.

For developers building or extending LLM inference systems, this gives a clear mental model of how vLLM orchestrates scheduling, memory, and execution. It’s the first in a series, so expect deeper dives into individual subsystems later.