Models & Labs
d-Matrix Integrates NVIDIA NVLink for XPU Deployment
NVIDIA Bloghigh confidence
Why it matters
- →NVLink Fusion enables seamless integration of custom XPUs with NVIDIA's AI platform.
- →This partnership reduces deployment risks and accelerates time to market for AI solutions.
- →It highlights the trend of integrating specialized processors into unified AI ecosystems.
d-Matrix has announced its adoption of NVIDIA's NVLink Fusion to connect its Raptor XPUs with NVIDIA's AI infrastructure platform. This integration allows d-Matrix to utilize NVIDIA's MGX rack architecture and networking solutions, facilitating a more efficient deployment of their custom silicon at scale. The partnership aims to provide a faster, lower-risk path for deploying ultralow-latency inference solutions. By aligning with NVIDIA's AI platform, d-Matrix can enhance its capabilities in AI factory environments, offering improved performance and scalability.
Read originalThe story around this
Earlier coverage that leads up to this article, and what followed. Lines connect each piece to the closest one after it, converging here.
More in Models & Labs
Models & Labsmodels
llama.cpp b12029 adds CUDA 13 and Snapdragon support
This release quietly cements llama.cpp as the universal inference runtime by finally supporting NVIDIA's latest CUDA 13 stack alongside AMD's ROCm 10.0. The inclusion of native builds for Linux arm64 with Snapdragon chips, covering CPU, Adreno GPU, and Hexagon NPU, signals a serious push into mobile edge deployment. While Apple Silicon KleidiAI is disabled in this specific build, the broader hardware coverage means developers no longer need to compile from source for most modern accelerators. It’s less about new features and more about removing friction for anyone running local models on bleeding-edge or diverse hardware.
llama.cpp Releases
© The Verge AIModels & Labsother
