LLM inference engine
Production vLLM infrastructure, deployed and operated for you.
vLLM is a high-throughput inference and serving engine for large language models, widely used in production for its efficient memory management and continuous batching. GPUMarket provisions the GPU infrastructure and operates the vLLM deployment so your team can focus on the model, not the cluster.
What is vLLM?
vLLM is an open-source inference server built around PagedAttention, an efficient memory-management technique for the key-value cache used in transformer inference. It supports continuous batching, tensor parallelism across multiple GPUs, and an OpenAI-compatible API, which makes it a common default for teams self-hosting LLMs in production.
GPUMarket is an independent infrastructure provider and is not affiliated with the vLLM project. We provide and operate the GPU infrastructure it runs on.
What we manage for you
- GPU selection and sizing for your target model and throughput
- Container image builds and version management
- Tensor-parallel and multi-GPU configuration
- Autoscaling based on request volume
- Health checks, monitoring and alerting
- Model updates and rolling deployments
- Network exposure, TLS and access control
Ideal for
- Teams serving open-weight LLMs (Llama, Qwen, Mistral, and similar) in production
- High-concurrency inference APIs
- Organizations that need an OpenAI-compatible internal endpoint
Recommended GPUs
Related solutions
Frequently asked questions
Does GPUMarket develop or maintain vLLM?
No. vLLM is an independent open-source project. GPUMarket provides and operates the GPU infrastructure required to run vLLM in production, including deployment, scaling, monitoring and support.
Which models can I serve with managed vLLM?
vLLM supports most popular open-weight model families. Tell us which model and expected throughput you need, and we will size GPU capacity and configuration accordingly.
Can managed vLLM run inside our own VPC or data center?
Yes. Managed vLLM can be deployed on shared or dedicated cloud capacity, in a customer VPC, or on private infrastructure as part of our Private AI solution.