Bring the training repo
Keep the framework and model code your researchers already use. No rewrites.
Train frontier models without the infra team
Take a novel architecture from a one-GPU experiment to a frontier run without being locked into one vendor's stack. We'll take care of the kernels, finding the hardware and running the fleet for you.
Research per dollar
3× more runs
From the same compute budget, on average across lab workloads.
GPUs, one workflow
1-10,000 GPUs
From a small experiment to a full pre-training fleet.
Kernel performance
3.55×
Faster than NVIDIA's baseline on its own kernel benchmark. #1 overall
The lab advantage
Your runway should go into research, not infrastructure.
The scarce thing is the architecture and the people inventing it. We make the compute interchangeable, compile kernels and operate your fleet so that a small team moves faster than a big lab.
Compile for the fleet
We search for the fastest kernel for each operation on each target and formally prove it correct.
Scale across supply
We place each job on the best-priced mix of hardware, providers, and regions.
Run and iterate
Launch from a Slack message or a commit, and track every run in one place. If a node fails, the run resumes from its last checkpoint on its own.
Built for research
Built for research that doesn't fit a template
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.
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.
Where it starts
Build a model for your business? See enterprises Give every research direction a credible path to scale
01
Experiments and ablations
You can launch runs from a Slack message or a commit, compare them side by side and automatically stop the ones that aren't working early.
Faster iteration with fewer wasted runs
02
Frontier pre-training
Scale what worked to thousands of GPUs, whether it's a dense model, an MoE, whether it's multimodal or an architecture that no one has named yet.
Clean path from ablation to full-scale pre-training
03
Post-training an RL
Whether you need to run SFT, RL with fast rollouts, need isolated sandboxes for agentic environments or have a novel post-training algorithm no one's ever tried before, we'll make sure it runs, fast.
Hardware choice without a permanent software commitment.
What would you train if compute weren't the constraint?
Leave the compute and infrastructure to us, so you can build the models you've been waiting to train.