Novel architectures, proprietary data, frontier scale. We handle everything between your idea and the silicon to let you iterate at the speed of light.
Whether you're a lab training something that doesn't exist yet or a company training a model on what only you know, we'll let you focus on the outcomes instead of the infrastructure.
We build faster kernels than anyone else can and frontier-scale infrastructure that recovers on its own, so every GPU-hour and researcher-hour goes into your model.
Bring your own team or alongside our embedded researchers. Either way, we bring the infrastructure and expertise to build a model you own that knows your domain as well as you do.
Choose the model, data, budget and deadline. SF Tensor turns fragmented compute into a reliable training system.
We achieve more with the same compute budget optimizing every layer from your training loop to the kernels.
Our optimizer keeps searching for faster kernels as your model evolves, so new architectures don't mean slow ones.
Take the experiments that worked to 10,000 GPUs across NVIDIA, AMD, TPU or Trainium without rewriting a line of code.
We think incentives need to be aligned, so every engagement is scoped to your goals and priced around the outcome you care about.
For AI labs that want to go from first experiment to frontier-scale run.
You only pay us a share of what we save you, which means you pay nothing if your bill doesn't go down.
| Feature | Self-managed cluster | Managed GPU cloud | Model Foundry |
|---|---|---|---|
| Kernels | Hand-tuned by your team | Vendor defaults | Searched and proven for your architecture |
| Hardware | One provider | One provider | Best-priced across NVIDIA, AMD, TPU, Trainium |
| Failure recovery | Manual | Partial | Automatic |
| Scale | Your cluster | Your quota | 1 to 10,000 GPUs |
| Infra team needed | Yes | Some | None |
| You pay | Full compute bill | Full compute bill | A share of the savings |
Our researchers embed with your team for a fixed engagement, with compute billed separately. You get a production model in about six weeks, then take it over or keep us running it.
Talk to an engineerBring the model, data and ambition. We'll bring the best stack to deliver the training outcome.