AMD Unveils Threadripper Halo Station to Run Trillion-Parameter AI Locally

By Carlos Montiel | Enterprise AI Specialist
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Published: 2026-09-12 | By: Carlos Montiel | Reading time: ~4 minutes

At the IFA 2026 trade show in Berlin on September 4th, AMD unveiled the Threadripper Halo Station: a desktop workstation built to run AI models with more than a trillion parameters entirely on-premise, with no dependency on the cloud.

96 Cores and Up to 576GB of Memory for AI

The system, announced by Jack Huynh, senior vice president and general manager of AMD's Computing and Graphics group, is built on the Threadripper PRO 9995WX processor with Zen 5 architecture, featuring 96 cores and 192 threads, up to 2TB of eight-channel DDR5 memory, and 128 PCIe Gen5 lanes. On top of that sit up to four AMD Instinct MI350P accelerators, each with 144GB of HBM3E memory at 4TB/s of bandwidth — in the base two-card configuration, the system already delivers 288GB of GPU memory, expandable to 576GB with all four cards installed.

Why Everything Is Liquid-Cooled

Both the processor and each MI350P accelerator carry independent liquid cooling, a departure from the air-cooled norm for workstations that reflects the heat AMD expects this configuration to generate under sustained AI workloads. Each MI350P card draws up to 600W.

Key specs (IFA 2026, Sep 4): - CPU: Threadripper PRO 9995WX, 96 cores / 192 threads, Zen 5 - System memory: up to 2TB DDR5, 8 channels - GPU: up to 4x Instinct MI350P, 144GB HBM3E each - Total GPU memory: 288GB (base) to 576GB (max) - Cooling: liquid, independent per processor - Target: running 1T+ parameter models locally

No Price or Ship Date Yet

AMD did not announce a price or final sales configuration. The system will become available sometime through 2027, and given its components, the cost is expected to land in the hundreds-of-thousands-of-dollars range — clearly a product for research labs and large AI developers, not the end user.

What this means for a company that uses or buys AI: if your company handles highly sensitive data (healthcare, banking, government) and has avoided generative AI because it can't send information to the cloud, this kind of hardware is a signal that running frontier models fully on-premise will stop being exclusive to hyperscalers. It isn't a purchasable option today — it ships sometime through 2027 with no public price — but it's a good moment to start evaluating whether your use case would justify a medium-term investment in local compute instead of depending 100% on cloud APIs.
Carlos Montiel
Enterprise AI Solutions Architect
Specialist in LLMs, Agents and Orchestration
guatemalia.com/en/#contacto · info@guatemalia.com

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