GPU baseline
SGLang / vLLM on GPUs
~100tok/s
Unchecked output
An infra-first test-time scaling company
Think further Finish sooner
Low-latency infrastructure for deeper reasoning and verification.
Talk to usTest-time scaling is the new frontier
- Pre-training: Learn from data.
- Post-training: Use capabilities better.
- Test-time scaling: Reason, explore and refine on each problem.
Our vision
1,000× more test-time compute, put to good use
Latency
Deep, recursive workflows have to finish in practical time.
Economics
Low-latency execution has to stay affordable at scale.
Verifiability
Results from very long reasoning traces have to be checked.
Not only fast, but also verified
Chrono Infer (GPU only)
optimised inference on GPUs
~1,000tok/s
Verified output
Chrono Engine (Cerebras + GPU)
Cerebras wafer with GPUs
~5,000tok/s
Verified output
The same second, spent on more thinking
0 s1 s · deadline
GPU system1× speed
one attempt
ChronoFoundryup to 10× speed
a checked answer
Reason deeper
More reasoning steps fit inside the same deadline.
Explore alternatives
Sample several candidates and keep the best one.
Evaluate & refine
Leave time to check the answer before it ships.
Up to 10× lower latency, from GPUs to spatial chips
One portable stack, on Cerebras since 2021 and now on Tenstorrent and AMD GPUs. A new model in 7 days instead of 3 months; new hardware in a month instead of a year.
2–4×
lower latency than SGLang / vLLM
On B300 GPU servers
6–10×
lower latency than GPU systems
On Cerebras
Frontier economics through heterogeneous execution
Each part of a workload runs on the chip that suits it best, scheduled by AI.
State-of-the-art GPU
1×
baseline interactivity
+ WSE-3
6×
relative interactivity
+ WSE-3 Turbo
10×
relative interactivity
Fast enough to be careful
Verification keeps pace with generation, from the outputs to the reasoning itself.
Review every request in time
Watch the review demoVerify before it runs
Watch verification at speedNot another inference company
| Execution engines | Inference platforms | Heterogeneous compute | ChronoFoundry | |
|---|---|---|---|---|
| Core question | Run models faster | Deliver inference at scale | Match workloads to hardware | How much useful compute, and is the result good enough? |
| Focus | Kernels, runtimes | API, capacity | Orchestration | Low-latency execution, search, verification |
Build with us
Infrastructure partners
Deploy our stack on your GPUs or spatial chips and serve fast tokens instead of renting raw hours.
Become a partnerQuestions
01What is test-time scaling?
Spending more compute while a model works on a problem, to reason longer, try alternatives and verify results, without retraining it.
02Do you train models?
No. We build the infrastructure that runs models faster and checks their results.
03Which hardware do you support?
Cerebras, Tenstorrent, and NVIDIA and AMD GPUs. Support for more is planned.
04How is this different from an inference provider?
Inference providers sell tokens. We focus on how much useful compute a task gets and whether its result can be trusted.
05Can I use my existing GPUs?
Yes, as an infrastructure partner. Compatibility is confirmed in a technical assessment.
06Are the speed figures guaranteed?
No. They come from specific benchmarks and setups. Results vary by model, hardware and workload.
ChronoFoundryInfrastructure for test-time scaling.