Search infrastructure that is 10x cheaper and 40x faster — by eliminating data movement at the hardware level.
3-year full TCO includes hardware or cloud rental, enterprise software licensing, and 3 years of power, colocation, and ops. H100 latency/throughput values are measured benchmark ranges; filtered-query GPU throughput reflects CPU offload overhead; NDPU throughput figures are pre-silicon projections.
Every vector database, every search engine, every AI retrieval pipeline today is built on the same 1980s skeleton: data lives in storage, moves to memory, then moves again to compute before anything happens.
That movement is the bottleneck. Not the algorithm. Not the index. The wire. Bandwidth keeps growing — the ceiling never disappears. It just moves.
We built a new kind of chip — the NDPU — where compute and storage are co-located. No data moves. Only results travel. This is a new architecture, not a software patch on top of old hardware.
Solving this requires thinking across software and hardware at the same depth. Most teams pick one. We don’t.
If you are building on AI search infrastructure at scale and want to go faster for less, we want to hear from you.