vLLM
App : A product you use directly
High-performance serving engine for running LLMs for multiple users in parallel.
The production choice as soon as multiple people are hitting the same model.
Security reflects where data goes, requested permissions and incidents on record at the verification date. It is not a guarantee of zero risk: check the deployment mode and the connections you enable.
What it does, concretely
Serving a language model to multiple simultaneous users creates memory and throughput bottlenecks: naive approaches over-allocate GPU memory and process requests one at a time. vLLM solves this by managing attention memory in a paged fashion (PagedAttention) and continuously batching incoming requests to maximize GPU utilization.
In practice, vLLM exposes a server compatible with the OpenAI API, as well as the Anthropic Messages API and gRPC. It supports more than 200 model architectures available on Hugging Face, including Llama, Qwen, Gemma, Mixtral, and DeepSeek-V3. Quantization (FP8, INT4, GPTQ, AWQ, GGUF, etc.) and speculative decoding help reduce memory usage and speed up inference.
vLLM targets technical teams deploying open models in production on Linux servers with NVIDIA, AMD, or Intel GPUs, or even on x86, ARM, or PowerPC CPUs. It also suits researchers who need high throughput for large-scale experiments.
However, vLLM is not suited for general audiences without system administration skills: installation and configuration are done entirely via the command line, and dedicated hardware (a GPU or powerful server) is generally required to benefit from it.
What it offers
- ·Paged attention memory management (PagedAttention) to reduce GPU memory waste when processing parallel requests.
- ·Continuous batching of incoming requests and chunked prefill to maximize throughput without waiting for a request to complete.
- ·Server compatible with the OpenAI API, Anthropic Messages API, and gRPC, usable as a drop-in replacement in existing applications.
- ·Support for more than 200 Hugging Face model architectures, including multimodal models, Mixture-of-Experts models, and embedding models.
- ·Quantization in FP8, INT4, GPTQ, AWQ, GGUF, and other formats to reduce memory usage and speed up inference.
- ·Distributed parallelism (tensor, pipeline, data, expert, context) to spread a model across multiple GPUs or machines.
- ·Multi-LoRA support to serve multiple model adapters simultaneously on a single instance.
- ·Speculative decoding (n-gram, EAGLE, DFlash) to speed up text generation without affecting output quality.
In practice
- Price
- Free
- Commercial use
- Commercial use: yes
- Available on
- linux, docker, cli
- Licence
- Apache-2.0
Security and data
- Rating
- Security: nothing to flag
- Your data
- Data stays local
- Maintenance
- Active
- Last release
- 18 October 2026
Links and repository checked on 25 October 2026 · added on 1 June 2023
How to install vLLM
uv pip install vllm