SF Tensor

Train custom machine learning models across multi-vendor GPU clusters at scale

Pricing model

Paid

Rating

★ People love it

Time to value

Requires Expertise

You can use SF Tensor to train custom artificial intelligence models across diverse hardware architectures without rewriting code. It manages multi-vendor hardware environments including NVIDIA GPUs, AMD chips, Google TPUs, and AWS Trainium. The system searches for faster custom kernels and automatically recovers from hardware failures during large training runs. Teams can run distributed pre-training, fine-tuning, and reinforcement learning jobs scaling up to 10,000 accelerators. It also offers embedded research support to develop domain-specific production models within weeks on private data.

What SF Tensor does

Configure distributed training runs across cross-vendor compute hardwareBenchmark custom neural network architectures against standard GPU kernelsDeploy fault-tolerant pre-training jobs spanning thousands of nodesFine-tune models using proprietary company datasets securelyExecute reinforcement learning algorithms on distributed cluster setupsSwitch training workloads across NVIDIA, AMD, TPU, and Trainium chipsTrack compute cost savings across large-scale training pipelinesCross-hardware execution across NVIDIA, AMD, TPU, and TrainiumAutomatic search and generation of faster custom kernelsAutomated failure recovery for uninterrupted frontier runsHorizontal scaling up to 10,000 accelerators without code adjustmentsSavings-share pricing based on compute cost reductionsHands-on embedded research team assistance for post-training