Tenstorrent designs AI accelerators that combine RISC-V CPU cores with their Tensix tensor compute units. The current product line is Wormhole (shipping) and Blackhole (recently launched). Led by CEO Jim Keller, a well-known chip architect whose prior work included AMD's Zen and Apple's A4 / A5. Tenstorrent is the rare datapoint in 2026 trying to make both the ISA and the accelerator open at the AI-accelerator level. The tt-metal low-level kernel framework and tt-forge model compiler are open-sourced under Apache 2.0. Compare to NVIDIA's H100 line, where the ISA, the accelerator, and the entire CUDA software stack are proprietary; or AMD's MI300X, where the hardware is proprietary but ROCm is mostly open. Tenstorrent sits further along the open spectrum than either. Production-readiness: shipping for real workloads, but the software ecosystem is years behind CUDA. Customers willing to do their own kernel work (national labs, sovereign-compute programs, research groups) can use it; mainstream production AI inference is still on NVIDIA. The strategic question for Tenstorrent is whether the open-ISA + open-accelerator argument attracts enough early customers and contributors to close the software gap before NVIDIA's lock-in compounds further.
The Stack · Silicon · Open source
Tenstorrent (Wormhole, Blackhole)
Open-trending AI accelerators on RISC-V; Jim Keller-led; tt-metal and tt-forge open.
Sources
- Tenstorrent — Products https://tenstorrent.com/hardware/
- tt-metal on GitHub https://github.com/tenstorrent/tt-metal
- tt-forge on GitHub https://github.com/tenstorrent/tt-forge
- Tenstorrent — Vision https://tenstorrent.com/vision
- tenstorrent.com (audit-verified) https://tenstorrent.com/en/hardware/wormhole
- tenstorrent.com (audit-verified) https://tenstorrent.com/newsroom/tenstorrent-launches-blackhole-developer-products-at-tenstorrent-dev-day
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Other projects at the Silicon layer
6 siblings · ordered open first
- RISC-V Open source
Open instruction set architecture; royalty-free; substrate for open silicon (CPUs and emerging AI accelerators).
- NVIDIA H100 / H200 Proprietary
Hyperscaler-class AI accelerator with CUDA software moat; default frontier-training and frontier-inference hardware.
- AMD MI300X / MI325X Proprietary
Highest-memory accelerator on the market (192 GB+ HBM); ROCm software stack open-source-adjacent.
- Cerebras CS-3 Proprietary
Wafer-scale accelerator; proprietary but disruptive on inference economics for specific model sizes.
- Groq LPU Proprietary
Language Processing Unit; proprietary; extraordinarily fast inference for small-to-medium models at low batch sizes.
- Apple Silicon (M-series) Proprietary
Unified memory architecture; closed silicon, but the strongest on-device inference platform via llama.cpp and MLX.