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Tenstorrent
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About Tenstorrent
Tenstorrent provides an open, sovereign AI compute platform spanning Blackhole accelerator cards, liquid-cooled workstations, TT-QuietBox servers, and licensable Galaxy IP. The TT-Forge compiler, built on MLIR, ingests PyTorch, JAX, and ONNX models to generate optimized kernels for Tenstorrent hardware, enabling consistent development from prototype to production. Teams can prototype on deskside systems, scale to rack-mounted servers, and license flexible IP to tailor accelerators for specific workloads without vendor lock-in. Thermal options include passive, active, or liquid cooling to balance performance, acoustics, and density constraints. The platform targets ML engineers, researchers, MLOps teams, and OEMs seeking deterministic performance, on-prem sovereignty, and a portable toolchain across development and deployment environments. It emphasizes inspectable kernels, open tooling, and a responsive developer community for iterative performance tuning and transparent optimization workflows.
Tenstorrent
North York, Canada
- Headquarters
- North York, Canada
Key features
- MLIR-based TT-Forge compiler for PyTorch, JAX, and ONNX models
- Blackhole accelerator cards for efficient AI workloads
- TT-QuietBox servers for sovereign, scale-out production deployments
- Galaxy IP licensable processor IP for custom silicon development
- Passive, active, or liquid cooling options for thermal flexibility
- Consistent toolchain from deskside prototypes to rack-scale systems
- Open-source compiler and developer community for transparent optimization
- Deterministic performance across workstations and servers
Use cases
- Prototyping large AI models locally on liquid-cooled workstations
- Deploying sovereign AI workloads in on-prem TT-QuietBox servers
- Licensing Galaxy IP to build specialized AI accelerators for targeted workloads
Pros
- Open-source compiler (TT-Forge) supporting PyTorch, JAX, and ONNX for model compilation
- Hardware portfolio includes deskside workstations, rack-mounted servers, and accelerator cards with passive, active, or liquid cooling options
- Licensable Galaxy IP enables custom silicon designs without vendor lock-in
- Emphasis on transparent, inspectable kernels and open tooling for developer control
- Multi-server Galaxy superclusters designed for near-linear scaling of AI workloads
Cons
- Hardware pricing and availability may vary by region and configuration
- Beta status of TT-Forge compiler may require user feedback for stability improvements
Frequently asked questions about Tenstorrent
What is Tenstorrent?
Tenstorrent provides an open, sovereign AI compute platform including hardware accelerators, workstations, servers, and licensable IP, alongside an open-source MLIR-based compiler (TT-Forge) for model optimization.
Who should use Tenstorrent?
The platform targets ML engineers, researchers, MLOps teams, and OEMs seeking deterministic performance, on-prem sovereignty, and portable toolchains across development and deployment environments.
What models does TT-Forge support?
TT-Forge supports PyTorch, JAX, ONNX, and other frameworks, enabling compilation of AI models for Tenstorrent hardware.
Does Tenstorrent offer hardware cooling options?
Yes, Tenstorrent provides passive, active, and liquid cooling options across its hardware portfolio to balance performance, acoustics, and density constraints.
How can I get started with Tenstorrent?
Users can install the TT-Forge compiler, explore open-source repositories, join the developer community on Discord, or purchase hardware like Blackhole workstations or TT-QuietBox servers.
Can I license Tenstorrent IP for custom silicon?
Yes, Tenstorrent offers flexible IP licensing for tailoring accelerators to specific workloads without vendor lock-in.
Tenstorrent Website Engagement
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