d-Matrix builds processors that perform AI calculations directly inside the memory where data is stored, rather than constantly shuttling data back and forth between separate memory and processing units. This approach dramatically reduces the energy wasted on moving data around, which is one of the biggest inefficiencies in running AI today.

Unlike GPUs that shuttle data between separate compute and memory units, d-Matrix performs calculations directly inside memory arrays using a digital (not analog) approach, maintaining precision while dramatically reducing data movement energy. This gives them both the efficiency benefits of in-memory computing and the reliability of digital circuits.

As AI inference workloads grow to dominate data center spending — projected to surpass training costs — d-Matrix's inference-optimized architecture offers compelling total cost of ownership advantages over GPU-based solutions. d-Matrix raised a $275M Series C in Q4 2025 led by BullhoundCapital, Triatomic Capital, and Temasek, with participation from Microsoft's M12 and the Qatar Investment Authority.