TensorPool

Train foundation models. We handle the infrastructure.

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Added Jul 2026

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What is Tensorpool?

TensorPool is an infrastructure platform that lets ML engineers train foundation models without managing complex infrastructure. It provides on-demand multi-node GPU clusters with high-performance shared storage (up to 300 GB/s read), managed through a simple Git-style CLI. Designed for teams and individuals running distributed training jobs, it handles cluster provisioning, scheduling, and resource optimization automatically.

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Key Features of Tensorpool

Multi-node GPU clusters on demand
Git-style CLI for job management
High-performance shared storage (300 GB/s read, 150 GB/s writes)
Always 3.2 Tbs Infiniband connectivity
Automatic scheduling and binpacking
Normal SSH access
tp cluster create for easy provisioning
tp storage create for shared volumes
tp job push for training job deployment

Who Should Use Tensorpool?

Train foundation models at scale

Run distributed training experiments

Manage multi-node GPU clusters

Optimize GPU utilization across teams

Accelerate ML model development

Tensorpool: Pros & Cons

✓Pros

Eliminates infrastructure management burden
Familiar Git-style interface for cluster management
Extremely high-performance storage infrastructure
On-demand capacity from multiple compute providers
Automatic scheduling reduces idle GPU time
Shared storage faster than local NVMe

Frequently Asked Questions about Tensorpool

What is TensorPool?

TensorPool is an infrastructure platform that provides on-demand multi-node GPU clusters with high-performance shared storage, managed through a Git-style CLI. It handles infrastructure provisioning so you can focus on training models.

What instance types are available?

TensorPool provides access to multiple GPU types including 8xB200 clusters. The platform partners with compute providers including Nebius, Lightning AI, Verda, Lambda Labs, Google Cloud, and Microsoft Azure.

How does TensorPool manage GPU clusters?

TensorPool uses a Git-style CLI with commands like tp cluster create, tp storage create, and tp job push. It automatically schedules and binpacks users onto operated clusters, eliminating the need for manual orchestration.

What storage performance does TensorPool offer?

TensorPool's shared storage infrastructure delivers up to 300 GB/s read, 150 GB/s writes, 1.5M read IOPS, and 750k write IOPS—faster than local NVMe with 10GB/s reads and 5GB/s writes per volume.

When should I use TensorPool?

TensorPool is ideal for teams and individuals training foundation models at scale who want to avoid infrastructure complexity while maintaining high performance and automatic resource optimization.