Faster inference means more reasoning in the same amount of time. That could bring some of the hardest problems in science and medicine within reach. Proud to be investors in @fractile_ai! Huge congrats to @goodwin_ml and the team!
We have raised a $720M Series C, led by @ThriveCapital, @lightspeedvp, @Redpoint and @foundersfund, to allow us to run faster in shipping the world's fastest chips for AI.View quoted post on X
We've seen over the past year that many of the hardest problems that humanity faces - in science, medicine, physics - may be solved in our lifetimes by AI.
One hallmark of AI applied to the toughest problems is that extreme scaling of inference compute becomes vital. In tackling a Millenium Prize problem, for instance, @OpenAI ran 10,000 parallel agents for 80 hours. Costing over $10M, this level of capability is far from democratised today.
When we study agentic scaling, there are two paths: parallel, and sequential. In just the same way that most hard problems solved by humans don't parallelise efficiently, we see the same with agents: fewer agents, thinking for longer, scales better in every way except for the time that you wait for a response.
Building radically faster inference hardware, which doesn't force a trade-off on throughput, model sizes or context lengths, is a key step in bringing down the cost and time we incur in solving our hardest problems.
We're thrilled to be backed to bring this to the world!
