Atlas

d-Matrix builds AI inference compute hardware and software for data centers, including inference accelerators, networking, and an orchestration stack designed to make generative AI faster, more…

Etched designs frontier inference clusters—specialized AI chips, racks, software, and manufacturing methods intended to run frontier models with high throughput, low latency, and better power…

Fractile designs AI inference chips, systems, and software intended to make frontier-model inference faster, more power-efficient, and lower-cost at scale.

FuriosaAI designs AI inference processors and systems intended to run models with less power and cost than general purpose GPUs.

Groq is a U.S.-based AI inference company that designs specialized LPU chips and builds GroqCloud and related systems to run AI models faster and more affordably at scale. (groq.com)

Lamb Labs builds model processing units and related software to make AI inference faster and more power efficient by hardcoding models into FPGA fabric and custom silicon.

OLIX is a photonic AI inference hardware company building the DX-1, a decode-focused accelerator and rack-scale system designed to improve inference throughput, latency, and energy efficiency.

Positron builds purpose built hardware and systems for AI inference, including its Atlas appliance and upcoming Titan and Asimov silicon, with a focus on higher performance per dollar and lower…

Q.ANT develops photonic processors and computing systems that use light to accelerate AI and high performance computing workloads with lower energy use.

Sparxity builds sparse-native AI computing systems, combining custom chips, boards, software, and rack scale systems to run inference more quickly and efficiently.

Volantis builds photonic AI inference hardware and system architecture meant to raise memory bandwidth and lower inference cost for very large models.