cedana / throughput · v1.4 · build 2826

Cedana is the compute multiplier for frontier inference.

We increase tokens per GPU-hour at fleet-level scale. Transparent checkpoint, migrate, and resume turns GPU-hours lost to cold starts, failures, idle windows, and stranded placement into delivered tokens. Real-time GPU virtualization optimizes runtime execution to increase token throughput. These proprietary primitives deliver new capabilities for inference.

01 · Outcomes

Expand your existing capacity without new GPUs

Fleet output is useful GPU-hours times tokens per hour. Cedana raises both: lost hours become delivered tokens, and working hours deliver more. Same hardware, same models, same serving stack.

01

Recover lost GPU-hours

Cold starts, idle windows and failures stop costing time, and each working hour delivers more.

01

Frontier models resume 10 to 40x faster.

An 8-GPU, trillion-parameter model restores from checkpoint in about 60 seconds instead of a 10 to 40 minute cold start. Autoscaling provisions workloads closer to demand, not ahead of it.

02

Idle GPU windows convert to finished work.

A new spot tier detects idle windows across the fleet and runs long-horizon, stateful inference through them. When the GPU is reclaimed, the job moves with its state. No preemption window required.

03

Failures, patches, and rebalancing run as policy.

A node fails and the job resumes on healthy hardware. A security patch lands and workloads pause, then resume where they left off. Priorities change and jobs follow them. Fleet-wide, with no loss of state, including in-flight sessions.

04

Each GPU delivers more tokens per hour.

Runtime execution is optimized beneath the engine for 10 to 20% more token throughput on frontier models. It stacks on top of your existing speculative decoding and kernel optimizations.

02

At fleet scale, on your stack

The same mechanism from one GPU to the fleet, with no changes to models, engines or orchestrators.

05

Built for frontier inference at fleet scale.

Scale across single GPU, multi-GPU and multi-node. Prefill and decode pools autoscale independently and rebalance in seconds.

06

Scale agentic workloads with less VRAM

Route each task to the right model and escalate to a frontier model only when the task needs it. Cedana swaps models in and out of VRAM in seconds increasing tasks productivity by over 2x at lower costs.

07

Full-stack inference out of the box.

Bring your own stack, or start with a complete frontier-scale stack: NVIDIA Dynamo, vLLM and SGLang support, and Cedana Storage Express.

02 · Proof

Compute multiplier in action

Same hardware, same models, native versus Cedana. Every number links to the run it came from.

  1. BenchmarkCold startsFour frontier models on 8× B200 · 57–70 s restore vs 9.4–34.2 min native
    29×faster restore
    View benchmark →
  2. BenchmarkAgentic workloadsFour coding models on 8× RTX PRO 6000 · 14 GPUs of models served on 8 · 52% lower cost
    2.1×tasks per hour
    View benchmark →
  3. Interactive replayIdle spot tier on Slurm24-hour run, 8 GPUs · 588 preemptions · 8 of 8 jobs completed
    87%of idle time converted
    Open the replay →
  4. Interactive replayIdle spot tier on Kubernetes7.4-hour run on Amazon EKS · 13.2 s median handover to the priority job
    73%of idle time recovered
    Open the replay →
  5. Write-upModel portfolio on one nodeNVIDIA Dynamo and NeMo Switchyard · each swap costs 3.4% of the cold start it replaces
    1,891 GiBon a 1,341 GiB node
    Read the write-up →