
Fractile is building chips to run large language models two orders of magnitude faster. Existing hardware is good for training LLMs, but very poorly suited to subsequent inference of the trained…

Fractile is building chips to run large language models two orders of magnitude faster. Existing hardware is good for training LLMs, but very poorly suited to subsequent inference of the trained…
What they do: Build processors and full-stack systems that fuse memory and compute to accelerate LLM inference
Performance claim: Up to 25x faster inference and ~1/10th the cost (as stated by the company)
Founded: 2022
Notable funding: $220M Series B (May 2026 reported)
Headcount: 83 employees (reported)
AI inference hardware — reducing latency and cost of running large language models.
2022
Computer Hardware Manufacturing
$15M (reported)
Reported seed round following exit from stealth; participating investors reported include Kindred Capital, NATO Innovation Fund, and Oxford Sciences Enterprises
£5M
ARIA Scaling Compute programme grant (reported)
$220M
Reported lead investors named
“Factorial Funds, Accel, Founders Fund, Kindred Capital, NATO Innovation Fund, Oxford Sciences Enterprises, Innovia Capital, Cocoa, and several angel investors (reported)”
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Bristol or London, 3 days in the office, 2 days WFH At Fractile, we’re building what we believe will be the world’s fastest AI inference chip from the ground up. We’re balanced across hardware and software engineering, and HW/SW co-design is real here. We move fast, and we help each other move fast. We care about each other, the software we ship, and the people who rely on it.
To validate behaviour, our functional simulator allows us to iterate at scale with real software before the hardware exists. It runs across multiple machines, supports large experiments, and helps verify the correctness of our low level firmware, runtime stack and compiler lowering.
You’ll be there for the first end-to-end runs against realistic device behaviour, the debugging sessions that unblock whole teams, and the moments where simulator fidelity turns into better software, better silicon decisions, and faster bring-up.
What You’ll Do
What We’re Looking For
Nice to have
If you want to build the simulator that makes rack-scale software real before rack-scale hardware exists, come build it together.
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