Taalas’ announcement arrives weeks after Nvidia‘s Christmas Eve deal to license intellectual property from chip startup Groq for $20 billion, which reignited interest in a crop of startups and technologies used to perform specific elements of AI inference, the process where an AI model, such as the one that powers OpenAI’s ChatGPT, responds to user queries.
Taalas’ approach to chip design involves printing portions of an AI model onto a piece of silicon, effectively producing a custom chip suited for specific models such as a small version of Meta’s known as Llama. The customized silicon is paired with large amounts of speedy but costly on-chip memory called SRAM, which is similar to Groq’s design.
But it’s the bespoke design for each model that gives the Taalas chip its advantage.
“This hard wiring is partly what gives us the speed,” CEO Ljubisa Bajic told Reuters in an interview.
The startup assembles a nearly complete chip, which has roughly 100 layers, and then performs the final customization on two of the metal layers, Bajic said. It takes TSMC, which Taalas uses for manufacturing, about two months to complete fabrication of a chip customized for a particular model, he said.
It takes roughly six months to fabricate an AI processor such as Nvidia’s Blackwell.
Taalas said it can produce chips capable of running less sophisticated models now and has plans to build a processor capable of deploying a cutting-edge model, such as the GPT 5.2, by the end of this year.
Groq’s first generation of processor used an SRAM-heavy approach to its chip design, as does another startup, Cerebras, which signed a January cloud computing deal with OpenAI, and D-Matrix.