AMD Acquires AI Chip Startup Taalas to Boost Inference Performance by Etching Models Into Silicon
August 7th, 2026Via: Register:
In AMD’s latest bid to upset Nvidia’s dominance in AI hardware, the House of Zen has acquired AI chip company Taalas, which bakes model weights directly into silicon in a process that promises to boost inference performance by an order of magnitude or more.
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The startup’s chips don’t rely on HBM to store the model weights but rather etch them directly into the silicon. In a sense, Taalas’ chips are really model-specific integrated circuits or MSICs.
Perhaps more importantly, Taalas’ tech isn’t just conceptual. In February, the startup revealed its first test chip fabbed on TSMC’s 6nm process tech, which it called the HC1. Initial benchmarks saw the chip serve Meta’s Llama 3.1 8B at a blistering 16,960 tokens a second — when announced last February, that was 48x faster than Nvidia’s GPUs and 8.5x faster than Cerebras’ accelerators.
While Llama 3.1 is ancient by today’s standards, having made its debut all the way back in mid 2024, the reticle-sized chip was really intended to prove the concept.
Taalas has been incredibly secretive about how its chips actually work, but we know its processors are comprised of two main regions: the mask-ROM recall fabric where model weights are etched, and the SRAM recall fabric where KV caches and fine-tuning adapters are stored.
For its second-gen HC2 chip due out this summer, Taalas aims to boost parameter count to 20 billion parameters. That might not sound like much, but just like with GPUs for larger models, weights are simply distributed across multiple accelerators using pipeline parallelism.
At 20 billion parameters per chip, you’d need just 50 accelerators to support a trillion-parameter model, and AMD just so happens to have a rack-scale compute platform and in-house system design team that can comfortably accommodate that.
That’s quite a bit more space and power efficient than Nvidia’s recently unveiled LPX systems, which would need a few dozen GPUs and at least 2,000 Groq LPUs to serve the same model.
