Dedicated Inference

Pick the GPU class that fits your latency, throughput, and cost targets. Single-node or distributed multi-node serving, billed per GPU-hour.

Point deployments at fine-tuned checkpoints, custom architectures, or OSS weights in CoreWeave Object Storage.

vLLM and SGLang runtimes with OpenAI-compatible endpoints out of the box. Swap models, runtimes, or GPU classes without rebuilding the serving stack.

A tenant-isolated gateway runs authentication, load balancing, and request routing across replicas. Optimize for latency, data locality, or compliance.

Per GPU-hour billing against your chosen GPU class and capacity model. No egress fees, no ingress fees, no service markup.

Bring your own weights, select your GPU, and ship to a live endpoint. CoreWeave operates everything between your model artifact and your users. No cluster setup, no Kubernetes expertise, no infra team required to maintain the serving layer.

Pay-per-GPU-hour billing against explicitly chosen GPU classes means cost stays tied to infrastructure decisions, not abstract consumption units. Existing CoreWeave reserved nodes can be directed to Dedicated Inference at contracted rates without incremental fees.

Weights stored in CoreWeave AI Object Storage deploy directly without data movement, whether you trained them on CoreWeave or brought them in. Just bring your weights to Dedicated Inference for production on the same infrastructure and runtimes.