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.
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!
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!
