Master Tenstorrent with our step-by-step tutorial, detailed feature walkthrough, and expert tips.
Explore the key features that make Tenstorrent powerful for no-code builders workflows.
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.
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.
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.
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.
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.
Now that you know how to use Tenstorrent, it's time to put this knowledge into practice.
Sign up and follow the tutorial steps
Check pros, cons, and user feedback
See how it stacks against alternatives
Follow our tutorial and master this powerful no-code builders tool in minutes.
Tutorial updated March 2026