vLLM

What you can finish with vLLM

Each scenario is a full recipe: pain point → tool stack → steps → outcome.

• Run an LLM Locally: A Privacy Setup Where Data Never Leaves the Machine

  Sensitive data cannot go to a cloud API, yet you still need AI to process it — a seeming dead end.

  A local model running in 2 hours6 steps
• Ship an Agent: Deployment, Monitoring, and Cost Control

  It works locally and fails in production: cost runs away, errors go unnoticed, and nothing is traceable.

  Production-hardened in 2 days9 steps

Two ways in

Development path

Form factor：local-runtime

1. 2023-09-12PagedAttention and vLLM: virtual memory for the KV cache

   Kwon et al. bring OS virtual memory and paging to LLM serving: the KV cache is allocated in on-demand blocks with near-zero waste, and the vLLM serving system is built on top.

   https://arxiv.org/abs/2309.06180

Solid dots mark events whose subject is this tool; hollow dots mark events that mention it. All entries come from this site's timeline, where every event carries a reachable source.

First release: not stated. Not derived automatically. The earliest timeline event mentioning this tool may be a version milestone, a different product with the same name, or a passing mention rather than a launch; each needs checking against an official source, so this is left empty.

Our articles about vLLM

• Complete Guide to vLLM Deployment and Performance Tuning

  vLLM high-performance LLM inference engine deployment guide: installation, configuration, PagedAttention principles, performance benchmarking.

  2026-05-10 · 7 min
• Comparison of Other Inference Frameworks: A Complete Guide to llama.cpp, MLX, and ExLlamaV2

  An in-depth comparison of three major local LLM inference frameworks: llama.cpp (GGUF), Apple MLX, and ExLlamaV2, covering installation, performance, and use cases.

  2026-05-10 · 7 min
• LiteLLM Proxy Multi Model Routing in Practice

  Use LiteLLM Proxy to centrally manage multiple LLM backends such as DeepSeek, ModelStudio, Ollama, and more: load balancing, cost tracking, fallback configuration.

  2026-05-10 · 8 min
• Enterprise Data Security Basics: What Data Should Never Be Fed to AI

  Employees pasting customer personal data into free AI tools is one of the most common and hardest-to-prevent data leakage paths in enterprises. This article provides a four-tier data classification standard, a 'never upload' red-line checklist, a comparison of enterprise vs. consumer terms, local deployment options, and a question list for IT and legal teams.

  2026-09-30 · 8 min

Tools it is often compared with

• vLLMvsOllama
• vLLMvsLM Studio
• vLLMvsllama.cpp

Alternatives