Approach

Two models with different compute shapes.

Sequence and omics models are limited by data curation and bursty experiments. Robot policies are limited by rollout throughput and interconnect bandwidth. A scheduler that treats both as generic GPU jobs wastes capacity on each. Sinewgrid plans clusters, storage and scheduling around these two shapes.

	Cell & mRNA	Robotics
Primary data	Single-cell expression matrices, perturbation screens, sequence libraries, assay readouts.	Multi-camera video, joint and force logs, simulator rollouts, teleoperation episodes.
Training pattern	Large pretraining on sparse, high-dimensional tables and sequences, then fine-tuning per program.	Vision-language pretraining, imitation learning, then reinforcement learning on continuous rollouts.
What limits throughput	Data curation, input pipelines for very large sparse matrices, and demand that follows experiment schedules.	Rollout generation, simulation throughput, and sustained interconnect bandwidth for large batches.
How progress is checked	Wet-lab validation. Results return to training as new data.	Held-out tasks on real robots. The sim-to-real gap is measured per task.
Safety review	Biosecurity screening of generated sequences and tiered model access.	Physical safety cases with defined stop conditions before real-world use.

Closed loops

Real-world results return to training, and the cluster stays busy.

Each lane sends measurements back into the next training run. The shared fabric handles scheduling, checkpoints, evaluation and telemetry for both.

Shared fabric SchedulerCheckpointsEvaluationTelemetryAccess control

Planning a training run?

Tell us the model, the data and the schedule. We reply with a capacity plan and the evidence behind it.

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