vLLM provides an optimized inference runtime for open-source language models with PagedAttention, continuous batching, and an OpenAI-compatible API, allowing teams to tune engine parameters for latency, throughput, and memory efficiency across different model and GPU configurations.
vLLM delivers state-of-the-art high-throughput serving.
Drop-in replacement for OpenAI-style clients and SDKs.
Expose key engine and server args to tune latency, throughput, and memory.
Deploy popular Hugging Face models for chat, completions, and more.
Additional Nebius infrastructure costs may apply. Use the Nebius Pricing Page to estimate your infrastructure costs.
Root access & custom setup. Maximum performance tuning. Direct hardware control.
Run on your own Kubernetes for horizontal scaling and upgrades as you grow.
Reliable AI infrastructure backed by top-tier NVIDIA GPUs, purpose-built for demanding inference workloads. Multiple deployment methods — virtual machines for full hardware control, Kubernetes for scalable cluster deployments, and managed serverless applications for teams that want inference running without infrastructure overhead
Learn about Nebius AI CloudNebius meets a broad set of security and compliance standards. Fine-grained IAM controls, audit logs, and encrypted storage are available out of the box — so teams can meet security requirements without additional tooling.
Explore the Trust centerApplication support
Provided by vLLM Project. See the documentation and project links above.
Infrastructure support
Provided by Nebius for the underlying cloud infrastructure.
