AI hardware acceleration platform providing chips, workstations, and open-source compiler tools for running AI workloads at scale.
AI hardware acceleration platform providing chips, workstations, and open-source compiler tools for running AI workloads at scale.
Tenstorrent is an AI infrastructure hardware company that designs open-source RISC-V based chips, workstations, and servers for running AI workloads at scale, with pricing starting at $999 for the Blackhole card. It targets AI researchers, ML engineers, enterprise infrastructure teams, and sovereign AI deployments seeking alternatives to proprietary GPU vendors without vendor lock-in.
Founded in 2016 and led by legendary chip architect Jim Keller, Tenstorrent offers a tiered product lineup designed to scale from individual developers to hyperscale deployments. The Blackhole™ card starts at $999 and can be added or configured into custom rigs with passive, active, or liquid cooling options. The TT-QuietBox™ workstation starts at $11,999, offering whisper-quiet liquid-cooled performance capable of running models up to 80 billion parameters from a desk. For production deployments, the Tenstorrent Galaxy™ servers provide sovereign, scale-out infrastructure. Based on our analysis of 870+ AI tools, Tenstorrent stands out as one of the few hardware vendors committed to fully open architectures — including open-source silicon IP that can be licensed without lock-in.
The software stack is anchored by TT-Forge™, an MLIR-based open-source compiler currently in public beta that works with PyTorch, JAX, ONNX, and other major ML frameworks. Compared to proprietary AI accelerator platforms in our directory like NVIDIA CUDA or Google TPUs, Tenstorrent differentiates through complete transparency — all repositories are open source on GitHub, the company runs an active bounty program rewarding community contributions (recent bounties include optimizing atan2, log1p, and sin/cos/tan operations), and IP can be licensed directly. Partnerships with Infinia Technologies for sovereign AI infrastructure and participation in the CHASSIS Program reinforce the company's position as an alternative for organizations prioritizing supply-chain independence and architectural openness.
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Entry-level AI accelerator cards starting at $999 that can be added to or configured within custom rigs. Cards ship with passive, active, and liquid cooling options to support different deployment environments, from quiet desktops to dense server racks.
A whisper-quiet, liquid-cooled workstation starting at $11,999 capable of running AI models up to 80 billion parameters directly from a desk. Targeted at researchers and developers who need LLM-scale inference without relying on cloud infrastructure or managing server-room deployments.
Sovereign, production-grade scale-out servers for enterprise AI workloads. Designed for customers prioritizing architectural transparency and supply-chain independence, making Galaxy suitable for government, financial, and regulated industry deployments where vendor lock-in is a concern.
An MLIR-based open-source compiler built on top of Tenstorrent's AI software stack, currently in public beta. Supports PyTorch, JAX, ONNX, and more, allowing developers to compile existing models for Tenstorrent hardware with framework-native workflows.
Tenstorrent licenses its chip IP transparently, allowing customers to embed Tenstorrent architecture into their own silicon. The company also runs an active bounty program where developers are paid for contributions such as optimizing math ops (atan2, log1p, sin/cos/tan) and typecast operations.
From $999
From $11,999
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Tenstorrent scheduled a May 1, 2026 livestream event titled 'Where AI Runs' unveiling AI solutions deployed at scale with validated architecture, benchmarks, and customer deployments. Recent announcements include a partnership with Infinia Technologies to build sovereign AI infrastructure, participation in the CHASSIS Program, and the unveiling of Tenstorrent's first-generation compact AI accelerator device. TT-Forge entered public beta during this cycle.
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