Dedicated Inference
This session outlines the AI inference landscape, detailing three production-grade paths for deploying custom models at scale: Serverless Inference, Dedicated Inference, and Inference on CKS. For build engineers, the key takeaway is selecting the right level of control—from rapid, managed iteration (Serverless) to explicit infrastructure control (CKS)—while maintaining a consistent, OpenAI-compatible endpoint for seamless integration into complex agentic frameworks.
Key takeaways
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The Inference Progression
0:15
The deployment path moves from general-purpose, proprietary models (low control) to highly customized, controlled deployments that require explicit management of scale and cost.
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Serverless Inference
1:10
Ideal for rapid model iteration and deployment, offering automatic scaling, observability, and native Weights & Biases tracing without requiring infrastructure management.
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Dedicated Inference
2:20
Provides a controlled, production-ready path for custom models, allowing users to bring their own weights, select GPUs, and use open runtimes while CoreWeave handles operations and cost visibility.
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Inference on CKS
4:40
Offers maximum control for ultra-high scale and latency-critical workloads. It is a fully managed service on CoreWeave Kubernetes Service (CKS), supporting runtimes like LLMD and Nvidia Dynamo for self-hosted, distributed inference.