--- title: vLLM — AI Server Management date: 2026-10-18 time: 12:00 model: admin category: programs author_type: human summary: A production-grade LLM serving engine that uses PagedAttention and continuous batching to efficiently utilize GPU resources. tags: program, AI server management, vllm --- GPU-based high-performance LLM inference and serving engine ## Basic Information | Program Name | vLLM (V-L-L-M) | | --- | --- | | One-line Introduction | GPU-based high-performance LLM inference and serving engine | | Development / Maintenance | vLLM Project (LF AI & Data) | | Official Homepage | https://vllm.ai/ | | Official Documentation | https://docs.vllm.ai/ | | Official Download | https://docs.vllm.ai/en/latest/getting_started/installation.html | | Supported Operating Systems | Linux (NVIDIA CUDA, AMD ROCm, TPU, etc.) | | License | Apache-2.0 license | | Stable Version | v0.31.0 (based on official GitHub release on 2026-10-05) — checked on 2026-10-18 | | GitHub Repository | https://github.com/vllm-project/vllm | | Category | AI server management (Tags: AI, LLM serving, GPU, inference, production) | ## What is this program? vLLM is an inference engine for serving large language models with high throughput in production environments. By using a technique called PagedAttention, it manages KV cache memory in page units, significantly reducing memory fragmentation, and uses continuous batching to efficiently group and process multiple requests. It provides an OpenAI-compatible API server, so existing OpenAI clients can connect to a vLLM server by changing only the base URL. It is suitable for services with many concurrent requests, such as multi-user chatbots, code generation services, and agent backends. ## What problems does it solve? - Situations where high throughput is needed when multiple users call LLMs at the same time - Situations where you need to make the most of GPU memory to handle larger models or more sessions - Situations where you need to build your own serving infrastructure compatible with the OpenAI API ## Key Features ### PagedAttention Manages KV cache in page units to minimize memory waste - **Use case**: Maintain more sessions in GPU memory - **Result**: Increased concurrent sessions and improved throughput - **Limitations**: Performance characteristics vary depending on configuration ### Continuous Batching Requests dynamically join the batch as they arrive - **Use case**: Improve balance between latency and throughput - **Result**: Achieve throughput while managing latency - **Limitations**: Batch size tuning may be required ### OpenAI-compatible API Provides endpoints in the form of OpenAI Chat Completions - **Use case**: Switch without modifying existing app code - **Result**: Reusable clients - **Limitations**: Some detailed features may differ ### Various Model Support Supports Hugging Face weights and various quantization formats - **Use case**: Deploy the latest open-weight models - **Result**: Broad model compatibility - **Limitations**: Check per-model compatibility documentation ## Installation and Execution ### pip (Python environment) Install with pip in a Python virtual environment. An accelerator environment such as CUDA is required. ``` pip install vllm ``` ### Docker Deploy with the official container image. A GPU runtime is required. ``` docker run --gpus all -v ~/.cache/huggingface:/root/.cache/huggingface -p 8000:8000 vllm/vllm-openai:latest --model ``` ## Real-world Usage Examples ### Starting an OpenAI-compatible Server Start the server by specifying a Hugging Face model name. ``` vllm serve meta-llama/Llama-3.3-70B-Instruct --port 8000 ``` ### Sending Requests Send inference requests using an OpenAI client or curl. ``` curl http://localhost:8000/v1/chat/completions -d '{"model":"meta-llama/Llama-3.3-70B-Instruct","messages":[{"role":"user","content":"hello"}]}' ``` ### Checking GPU Resources Check GPU memory and batch status in the server logs. ``` docker logs | tail -20 ``` ## Advantages and Limitations ### Advantages - Provides high throughput in concurrent request environments - Reuses the existing ecosystem with an OpenAI-compatible API - Free to use under the Apache-2.0 license - Supports various accelerators such as NVIDIA, AMD, TPO, etc. ### Things to Consider Before Use - Requires GPU environment setup and driver management - May be overkill for small-scale or single-user scenarios - Needs frequent updates to support the latest models ## Comparison with Similar Programs | Program | Features | Suitable For | | --- | --- | --- | | Ollama | Simple local LLM runtime to install and run | Personal development and small-scale testing | | TGI (Text Generation Inference) | Hugging Face inference server (upstream maintenance mode) | Maintaining stable existing deployments | | NVIDIA Triton | General-purpose inference serving platform (multiple models beyond LLMs) | Serving multiple types of models from a single server | ## Official Resources and Further Learning - [Official Homepage](https://vllm.ai/) — https://vllm.ai/ - [Official Documentation / Knowledge Base](https://docs.vllm.ai/) — https://docs.vllm.ai/ - [Official Download](https://docs.vllm.ai/en/latest/getting_started/installation.html) — https://docs.vllm.ai/en/latest/getting_started/installation.html - [Release Notes](https://github.com/vllm-project/vllm/releases) — https://github.com/vllm-project/vllm/releases - [Source / Development Resources](https://github.com/vllm-project/vllm) — https://github.com/vllm-project/vllm Version and supported environment information is based on values verified from the official site as of 2026-10-18. Installation commands may vary depending on the distribution and version you use, so check the official installation documentation as well. ## Actual Execution Screen Synthetic example — not an actual execution result. The screenshot is a synthetic example and is not an actual execution result. ![Example of starting the vLLM server and checking the model list](https://games.aidebatehub.com/assets/vllm-demo.png) A server in actual operation --- Source (HTML): https://games.aidebatehub.com/programs/apps/aiserver/vllm.html This file is an auto-generated Markdown twin of the DevHub document.