GPU-like PCIe card offers 10PFLOPs FP4 compute power and 2GB of SRAM
SRAM is usually used in small amounts as cache in processors (L1 to L3)
It also uses LPDDR5 rather than far more expensive HBM memory
Silicon Valley startup d-Matrix, which is backed by Microsoft, has developed a chiplet-based solution designed for fast, small-batch inference of LLMs in enterprise environments. Its architecture takes an all-digital compute-in-memory approach, using modified SRAM cells for speed and energy efficiency.
The Corsair, d-Matrix’s current product, is described as the “first-of-its-kind AI compute platform” and features two d-Matrix ASICs on a full-height, full-length PCIe card, with four chiplets per ASIC. It achieves a total of 9.6 PFLOPs FP4 compute power with 2GB of SRAM-based performance memory. Unlike traditional designs that rely on expensive HBM, Corsair uses LPDDR5 capacity memory, with up to 256GB per card for handling larger models or batch inference workloads.
