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Tenstorrent

AI hardware acceleration platform providing chips, workstations, and open-source compiler tools for running AI workloads at scale.

Starting atFrom $999
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In Plain English

AI hardware acceleration platform providing chips, workstations, and open-source compiler tools for running AI workloads at scale.

OverviewFeaturesPricingUse CasesLimitationsFAQAlternatives

Overview

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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Key Features

Blackhole™ AI Accelerator Cards+

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.

TT-QuietBox™ Liquid-Cooled Workstation+

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.

Tenstorrent Galaxy™ Scale-Out Servers+

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.

TT-Forge™ Open-Source MLIR Compiler+

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.

Licensable Open IP and Bounty Program+

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.

Pricing Plans

Blackhole™ Card

From $999

  • ✓Entry-level AI accelerator card
  • ✓Add to or configure custom rigs
  • ✓Passive, active, or liquid cooling options
  • ✓Compatible with TT-Forge compiler
  • ✓Open-source software stack

TT-QuietBox™ Workstation

From $11,999

  • ✓Whisper-quiet liquid-cooled workstation
  • ✓Runs models up to 80B parameters
  • ✓Desk-ready form factor
  • ✓Pre-integrated Tenstorrent software stack
  • ✓PyTorch, JAX, and ONNX support via TT-Forge

Tenstorrent Galaxy™ Server

Contact sales

  • ✓Sovereign, scale-out production servers
  • ✓Designed for enterprise AI workloads
  • ✓Rack-scale deployments
  • ✓Supply-chain independence
  • ✓Enterprise support available

IP Licensing

Contact sales

  • ✓License Tenstorrent chip IP
  • ✓Embed in your own silicon
  • ✓Transparent architecture
  • ✓No vendor lock-in
  • ✓Flexible terms per workload
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Best Use Cases

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Sovereign AI infrastructure deployments where domestic or vendor-independent hardware is a regulatory or strategic requirement

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On-premises LLM inference up to 80B parameters using the TT-QuietBox workstation as an alternative to cloud GPU rental

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Research labs and universities building custom AI accelerator clusters at lower capital cost than NVIDIA DGX systems

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Companies licensing AI chip IP to embed into their own silicon without royalty lock-in to proprietary architectures

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ML compiler engineers contributing to an open MLIR-based stack via TT-Forge PRs and the bounty program

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Enterprise AI teams piloting Blackhole cards in custom rigs before committing to full Galaxy server deployments

Limitations & What It Can't Do

We believe in transparent reviews. Here's what Tenstorrent doesn't handle well:

  • ⚠TT-Forge compiler is in public beta, so edge-case model compilation and optimization may require community workarounds
  • ⚠Ecosystem of pre-optimized kernels and third-party libraries is smaller than NVIDIA CUDA or AMD ROCm
  • ⚠No managed cloud offering — customers must purchase, install, and operate hardware themselves
  • ⚠Performance benchmarks for many workloads are still being validated against established competitors
  • ⚠Support for less common ML frameworks beyond PyTorch, JAX, and ONNX may require custom compiler work

Pros & Cons

✓ Pros

  • ✓Aggressive entry pricing with Blackhole cards starting at $999, dramatically lower than competing AI accelerators
  • ✓Fully open-source software stack and transparent IP available for licensing without vendor lock-in
  • ✓TT-QuietBox workstation runs up to 80B parameter models locally from a desk at $11,999
  • ✓Active bounty program pays developers for real contributions like optimizing math operations and typecast ops
  • ✓Broad framework support through TT-Forge MLIR compiler covering PyTorch, JAX, and ONNX
  • ✓Led by renowned chip architect Jim Keller with credibility in AMD Zen, Apple A-series, and Tesla chip design

✗ Cons

  • ✗TT-Forge compiler is still in public beta, meaning production stability and performance may lag NVIDIA's mature CUDA ecosystem
  • ✗Smaller developer community and fewer pre-tuned models compared to dominant GPU platforms
  • ✗Workstation entry at $11,999 remains a significant capital investment for individual researchers
  • ✗Limited third-party software ecosystem and cloud availability compared to established accelerators
  • ✗Documentation and tutorials still maturing as the hardware is relatively new to market

Frequently Asked Questions

How much does Tenstorrent hardware cost?+

Tenstorrent offers tiered pricing across its product line. The Blackhole™ AI accelerator card starts at $999, making it one of the most affordable entry points into dedicated AI hardware. The TT-QuietBox™ workstation starts at $11,999 and can run models up to 80 billion parameters locally. The Tenstorrent Galaxy™ scale-out server pricing is available by contacting sales, and IP licensing is negotiated per customer.

What frameworks does TT-Forge support?+

TT-Forge™ is Tenstorrent's MLIR-based open-source compiler built on top of the company's existing AI software stack. It is designed to work with PyTorch, JAX, ONNX, and other major machine learning frameworks. The compiler is currently in public beta, with the team actively soliciting feedback through pull requests and Discord. This makes it possible to compile existing models with minimal code changes.

How does Tenstorrent compare to NVIDIA GPUs?+

Tenstorrent positions itself as an open alternative to NVIDIA's proprietary CUDA ecosystem. While NVIDIA offers a more mature software stack and broader ecosystem support, Tenstorrent differentiates through open-source silicon IP, open architecture based on RISC-V, and significantly lower entry pricing starting at $999. Based on our analysis of 870+ AI tools, Tenstorrent is one of the few vendors allowing customers to license and modify the underlying chip IP directly.

Can I run large language models on Tenstorrent hardware?+

Yes. The TT-QuietBox™ workstation is specifically marketed as capable of running models up to 80 billion parameters from a desk, making it suitable for LLM inference and fine-tuning workloads. The Tenstorrent Galaxy™ server product scales further for production AI deployments. With TT-Forge support for PyTorch and ONNX, popular open-source models can be compiled and deployed on Tenstorrent silicon.

Is Tenstorrent's software actually open source?+

Yes, all of Tenstorrent's repositories are available on GitHub under open-source licenses. The company also runs a bounty program that pays external developers for merged contributions — recent examples include optimizing atan2, log1p, signbit, and typecast operations. This transparency extends to the hardware IP, which can be licensed and modified by customers. The stated mission is building an 'open future' with editable, forkable silicon.
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What's New in 2026

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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