SANTA CLARA, CA – 16/03/2026 – (SeaPRwire) – AI computing company Tenstorrent has introduced the TT-QuietBox
2 (Blackhole
), a liquid-cooled desktop AI workstation designed to run large-scale machine learning models locally while maintaining quiet operation suitable for office or laboratory environments. The new system enables developers and organizations to execute models containing up to 120 billion parameters directly on a workstation, offering teraflop-class inference performance in a compact desktop form factor.

The product represents what the company describes as the first desktop AI workstation based on RISC-V architecture to reach this level of inference capability. Starting at $9,999, the system is built with an entirely open-source software stack spanning the compiler, kernel, and developer tooling layers.
Inference Workloads Drive Demand for Local AI Compute
Industry trends have increasingly shifted toward inference as the dominant artificial intelligence workload. As organizations deploy trained models into production environments, the need to run inference workloads at scale has expanded rapidly. Estimates indicate inference workloads now represent more than half of global cloud AI infrastructure spending.
Developers frequently face two competing options: relying on cloud infrastructure that charges per-token usage fees, or deploying proprietary hardware systems whose software layers cannot be fully inspected or customized. The TT-QuietBox 2 platform is intended to address these challenges by giving users full visibility into the hardware and software stack supporting their workloads.
According to Jim Keller, CEO of Tenstorrent, the system was developed with open infrastructure and developer accessibility as central goals.
“Artificial intelligence software has matured to the point where developers can benefit from systems designed specifically for the complexity of modern workloads,” Keller said. “QuietBox 2 was created to provide a fast, quiet development platform that is open from hardware design to software stack, allowing developers to build and control their AI infrastructure.”
Designed for Real-World AI Workloads
The system arrives preconfigured to run several widely used AI models across different domains. For example, GPT-OSS 120B can operate locally on the workstation, enabling developers to run large language models privately without relying on external cloud services. Other supported models include Llama 3.1 70B, which can process tokens at high throughput, and Qwen3-32B for coding-focused applications.
Beyond language models, the workstation also supports multimodal and creative workloads. Image generation using Flux and video synthesis through Wan 2.2 can run locally on the device, allowing creators to maintain control over intellectual property without sending data to external servers.
Scientific computing represents another target use case. The biomolecular machine learning model Boltz-2 can predict the structure of complex proteins significantly faster than traditional CPU-based systems, enabling researchers to accelerate certain types of molecular modeling workloads.
To support broader compatibility, Tenstorrent provides TT-Forge, an open-source AI compiler capable of compiling models from frameworks such as PyTorch, ONNX, TensorFlow, JAX, and PaddlePaddle directly for the hardware platform.
Architecture Built Around Blackhole Processors
Inside the TT-QuietBox 2 system, four Blackhole ASIC processors operate together as a unified compute mesh. The configuration includes 480 Tensix cores capable of delivering approximately 2,654 teraflops of performance at BlockFP8 precision.
The hardware integrates compute resources with high-density SRAM on the same silicon die. This design allows tensors to move efficiently through on-chip memory, reducing reliance on external DRAM and minimizing bottlenecks commonly associated with memory bandwidth limitations in conventional accelerators.
The system includes 128 GB of GDDR6 memory and 256 GB of DDR5 system memory. By relying on GDDR6 and on-chip SRAM rather than High-Bandwidth Memory (HBM), the architecture avoids supply constraints that have affected many AI accelerator products.
TT-QuietBox 2 runs on Ubuntu 24.04 and operates using a standard 120-volt electrical outlet, eliminating the need for rack-mounted infrastructure, specialized cooling, or dedicated server facilities.
Fully Open-Source Software Stack
Tenstorrent has positioned openness as a core design principle for the system. Every major layer of the software environment is available as open source.
TT-Forge provides transparency into model graph optimization and execution, while TT-Metalium offers low-level control for developers seeking deterministic performance at the kernel level. TT-LLK manages low-level kernel software interactions.
This full-stack openness enables developers to analyze how models are executed on hardware, modify components of the software pipeline, and adapt the platform to specific workloads. Such transparency can be particularly relevant for research institutions, regulated industries, and organizations building sovereign AI infrastructure.
Desktop AI for Developers and Businesses
The system has been designed to balance performance with practical usability. It ships preconfigured with Ubuntu 24.04, developer tools, and the company’s TT-Studio environment to simplify deployment.
Engineering improvements have reduced idle power consumption and heat generation compared with earlier designs. The liquid-cooled enclosure is optimized for sustained workloads while maintaining quiet operation suitable for desk-side environments.
For small and medium-sized organizations, the workstation format may also provide a path to on-premises AI infrastructure without requiring dedicated server rooms or extensive IT resources.
Availability
TT-QuietBox
2 is scheduled for global availability in the second quarter of 2026, with pricing starting at $9,999. The system will be demonstrated during the Game Developers Conference 2026, taking place March 11-13.
About Tenstorrent
Tenstorrent is an artificial intelligence computing company focused on developing RISC-V-based processors and AI infrastructure systems for developers and enterprises. The company is led by semiconductor engineer Jim Keller, known for contributions to processor architectures including AMD Zen, Apple A-series chips, and Tesla’s Full Self-Driving hardware.
Tenstorrent has received backing from investors including Bezos Expeditions, Samsung, LG Electronics, Hyundai Motor Group, and Fidelity Investments. The company operates offices in Santa Clara, Austin, Toronto, Belgrade, Tokyo, Bangalore, Singapore, and Seoul.
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