Question 1

We've built our own warm-pool optimization. Why Cedana?

Accepted Answer

Most platforms have. None scales across the model catalog as model sizes grow. Cedana captures at the OS level, not the application or model layer, so the whole catalog is covered and your team stops maintaining caching code.

Question 2

What's the steady-state overhead during inference?

Accepted Answer

Low single-digit percent on representative workloads. We show live overhead on the call.

Question 3

Will it work with our autoscaler and orchestrator?

Accepted Answer

Yes. Cedana works with Slurm, Kubernetes, Kueue, Ray, Armada, and Nvidia Dynamo. Cedana plus Dynamo is the marketed product form factor for inference at scale. See the docs.

Question 4

How does it handle our serving framework (vLLM, SGLang, Triton)?

Accepted Answer

Cedana captures at the OS level, so the serving framework is irrelevant to the checkpoint layer. We show resume across vLLM and SGLang on the call.

Question 5

What about compliance and data handling at our scale?

Accepted Answer

Cedana operates on GPU memory state inside your existing security boundary. It is SOC 2 compliant, ships on-premise, and supports fully air-gapped deployments.

Question 6

Does it handle distributed multi-GPU inference?

Accepted Answer

Yes. Distributed multi-GPU coordinated checkpoint is the differentiating capability. We show an 8-GPU model-parallel job restored as one unit on the call.

Cold starts in seconds, not minutes.