Positron attacks Nvidia where GPUs waste memory, raises $230M before its ASIC ships

Positron AI builds energy-efficient hardware and software for running AI models, selling an inference appliance called Atlas that plugs into cloud or on-premise data centers.

Bet that winning inference hardware comes from exploiting memory bandwidth and capacity, not from out-FLOPSing Nvidia, and that FPGAs could prove it before an ASIC did.

$23.5M seed by early 2025, then a $75M Series A in June 2025, then a $230M Series B at over $1B post-money in Feb 2026 — just over $300M total (TechCrunch; Business Wire).

Thomas Sohmers and Edward Kmett, both alumni of inference startup Groq, founded Positron in April 2023 on one observation: transformer inference is limited by memory bandwidth and capacity, not compute, and GPU architectures waste most of both. They chose FPGAs so they could iterate with paying customers before committing to an ASIC.

Atlas began shipping in summer 2024, and EE Times reported first deliveries of a multi-million-dollar order in February 2025. Positron raised a $75M Series A in June 2025 and began designing Asimov, its own custom silicon. On Feb 4, 2026 it announced a $230M Series B at over $1B valuation, co-led by ARENA Private Wealth, Jump Trading and Unless, with QIA, Arm and Helena joining and existing investors Valor Equity Partners, Atreides Management and DFJ Growth participating.

As of Feb 2026 Positron is selling Atlas systems built in the US while racing to tape out Asimov by late 2026 and reach production in early 2027 — a bet that a memory-first chip can beat Nvidia's Rubin on tokens per watt and performance per dollar.

Transformer inference is memory-bound: GPUs sustain under 30% of their theoretical memory bandwidth on it, while Positron's FPGA design sustains 93% across use cases (EE Times).

FPGAs let the startup prove demand cheaply — Atlas shipped to paying cloud customers before Positron committed the years and capital an ASIC requires (EE Times).

A customer became the proof: Jump Trading measured roughly 3x lower end-to-end latency than an H100 system on its workloads, then stepped up to co-lead the round (Business Wire).

Energy is the next bottleneck in AI deployment, and Atlas claims H100-class performance at under a third of the power, so inference buyers have a reason to switch (TechCrunch).

Attack incumbents where they are weak: Positron targeted memory bandwidth, where GPUs waste most capacity, and won paying customers before its own ASIC existed.

After the Feb 4, 2026 announcement, Positron said Atlas was shipping to frontier customers across cloud and advanced computing and that it expected large-scale commercial traction within about 2.5 years of launch. The next test is Asimov: tape-out targeted for late 2026 and production in early 2027, with 2 TB of memory per accelerator and 8 TB per Titan system at rack-scale memory capacity over 100 TB. As of early February 2026 nothing had been taped out — the $1B valuation rests on Atlas's shipping traction and Jump Trading's customer-side benchmark, not on Asimov silicon in hand.