Train frontier ideas, not infrastructure teams.
Take a novel architecture from a local experiment to a frontier run without inheriting one vendor’s software stack. SF Tensor optimizes, verifies, places, and operates training across the best available compute.
Input
Optimize
Generate fast kernels
Scale
1 to 10,000 GPUs
Recover
Checkpoint and resume
Outcome
01. GPUs, one workflow
From local research to a production pre-training fleet.
02. Lower training cost
With cross-provider placement and workload-specific optimization.
03. Target fleet utilization
Through compiled kernels, topology-aware execution, and resilient input.
Research velocity should not depend on owning a hyperscaler.
The scarce thing is the architecture and the people inventing it. We make compute supply fungible by retargeting workloads across vendors, compiling kernels for the actual topology, and operating the fleet—so a small team can move like a large lab.
Live training efficiency
Fleet utilization
01
Bring the training repo
Keep the framework and model code your researchers already use.
02
Compile for the fleet
Search, generate, and formally verify optimized kernels for each target.
03
Scale across supply
Place the job on the best available mix of hardware, providers, and regions.
04
Run the experiment
Monitor, checkpoint, recover, and return the model—not an infrastructure backlog.
The systems team behind every training run.
Use SF Tensor as the compiler, performance, distributed systems, and fleet operations team that turns research code into a reliable frontier run.
Novel architectures welcome
Bring unusual layers, kernels, communication patterns, and research frameworks.
Automatic kernel optimization
Generate workload-specific kernels instead of waiting months for hand-tuning.
Proof before production
Use symbolic execution to prove optimized kernels equivalent or return a counterexample.
Cross-vendor portability
Retarget training as new accelerators and lower-cost supply enter the market.
Training-native storage
Keep data and checkpoints close to workers with shared, cluster-local caching.
Resilient fleet operations
Handle placement, observability, checkpoints, node failures, and run recovery.
Give every research direction a credible path to scale.
Foundation models
Pre-train dense, MoE, multimodal, and domain foundation models from scratch.
New architectures
Explore custom attention, state-space, memory, routing, and communication designs.
Alternative silicon
Use emerging accelerators when they are the best technical or economic fit.
Your model is the advantage. Infrastructure shouldn’t be the constraint.
Bring the model, data, and ambition. We’ll optimize the kernels, orchestrate the fleet, and deliver the training outcome.
Train the models only you can build. One stack for enterprise post-training and frontier pre-training.
All System Operational© 2026 San Francisco Tensor Company
