We announced that we are partnering with Nvidia to support rack-scale deployments of our 3D-DRAM enabled Raptor accelerators using the NVLink scale-up fabric on identical infrastructure to Nvidia’s Vera Rubin… Read More
Frontier-level models are now blowing past the trillion-parameter mark. And despite significant optimizations in inference, the urgent need for memory capacity is exploding with it. While small models continue to… Read More
In September 2026, d-Matrix and Nvidia announced a multi-year collaboration to bring d-Matrix inference accelerators into Nvidia’s MGX rack-scale architecture through NVLink Fusion. Two months earlier, Parasail began deploying d-Matrix… Read More
Modern GPUs, whether they’re just sitting there idling or running hot during inference, cost enormous sums to keep them from melting into a puddle of lost throughput potential. Those GPUs — drawing more than a kilowatt… Read More
Your reasoning agent just chained six tool calls, each one waiting on the last. Every millisecond of token generation latency compounds across that chain, and your user is waiting on… Read More
Enterprises constantly have to weigh the tradeoffs between maximizing the utilization of their GPUs and providing snappy, low-latency experiences. That tradeoff becomes an even worse nightmare when an AI application… Read More
Compute has raced ahead for two decades. Memory bandwidth hasn’t. Peak server compute has scaled roughly 3x every two years, while DRAM bandwidth has managed about 1.6x over the same… Read More
Running a single AI operation efficiently is one thing. Enabling a complete generative AI model—from its first computation through stateful, multi-token generation—is a much broader systems challenge. d-Matrix and Infinity… Read More
You added more GPUs and the numbers barely moved. Latency still spikes under load, cost per token won’t come down, and the dashboard says your chips are busy while throughput… Read More
Inference now costs more than training. Gartner forecasts that inference will account for 55% of AI-optimized cloud infrastructure spending in 2026—$23.3 billion against $19 billion for training—rising to 59% in 2027.… Read More
Disaggregated AI inference pipelines that split the pre-fill and decode process across different hardware—like two different GPUs or a GPU and a custom accelerator—already substantially speed up AI inference and… Read More
