Gimlet Labs raises $300M Series B at $3B valuation (a16z-led).

The problem: Agentic AI chains dozens of model calls. Each phase needs different hardware—prefill (compute-bound), decode (memory-bound), tool calls (network-bound). No single chip handles all three efficiently.

Gimlet's solution: Disaggregate inference across heterogeneous silicon. Their software maps each workload phase to optimal hardware (GPUs, SRAM accelerators, CPUs), delivering 3-10X faster inference at same power.

Why it matters: Token demand grows 20X by 2030. Power is the bottleneck. Maximizing throughput per kW is now existential.

https://gimletlabs.ai/blog/announcing-series-b

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