The round was co-led by Kindred Capital, NATO Innovation Fund, and Oxford Science Enterprises, with participation from Cocoa and Inovia Capital, together with angel investors including Hermann Hauser (co-founder, Acorn, Amadeus Capital), Stan Boland (ex-Icera, NVIDIA, Element 14 and Five AI), and Amar Shah (co-founder, Wayve). To date, Fractile has raised $17.5m (£14m) in total funding.
Founded in 2022 by 28-year-old artificial intelligence PhD, Walter Goodwin, Fractile has developed a radically different approach to the design of chips for AI inference that can deliver transformational improvements in performance for frontier AI models in deployment.
AI chip
There are two paths available to a company attempting to build better hardware for AI inference.m, says Fractile. The first is specialisation: honing in on very specific workloads and building chips that are uniquely suited to those specific requirements.
Because model architectures evolve rapidly in the world of AI, whilst designing, verifying, fabricating and testing chips takes considerable time, companies pursuing this approach face the problem of shooting for a moving target whose exact direction is uncertain.
The second path is to fundamentally change the way that computational operations themselves are performed, create entirely different chips from these new building blocks, and build massively scalable systems on top of these. This is Fractile’s approach, which will unlock breakthrough performance across a range of AI models both present and future.
Inference
Fractile claims its system will achieve 100x faster and 10x cheaper inference performance by using novel circuits to execute 99.99% of the operations needed to run model inference.
A key aspect is a shift to in-memory compute, which removes the need to shuttle model parameters to and from processor chips, instead baking computational operations into memory directly. Fractile is also unique in its approach to building these computational units, while still ensuring its technology is fully compatible with the leading-edge unmodified silicon foundry processes that all leading AI chips are built on.
Not only will Fractile provide vast speed and cost advantages, but it does so at a substantial power reduction. Power – sometimes measured in Tera Operations Per Second per Watt (TOPS/W) – is the biggest fundamental limitation when it comes to scaling up AI compute performance. Fractile’s system is targeting 20x the TOPS/W of any other system visible to the company today.
This allows for more users to be served in parallel per inference system, with – in the case of LLMs for example – more words per second returned to those users, thereby making it possible to serve many more users for the same cost.
