I'm using multiple GPUs in a Proxmox home lab setup for AI inference, video processing, and more, breathing new life into old hardware.

The Fenghua No. 3 is based on the open-source RISC-V architecture, with inputs from the OpenCore Institute's Nanhu V3 project. The new design is expected to ...

AI developers use popular frameworks like TensorFlow, PyTorch, and JAX to work on their projects. All these frameworks, in turn, rely on Nvidia's CUDA AI toolkit and libraries for high-performance AI ...

More games, more power, more AI-generated frames. More games, more power, more AI-generated frames.

NVIDIA's CUDA Toolkit 13.0 introduces innovative features like tile-based programming and unified Arm platform support, enhancing developer productivity and GPU performance. The latest iteration of ...

Apple’s MLX machine learning framework, originally designed for Apple Silicon, is getting a CUDA backend, which is a pretty big deal. Here’s why. The work is being led by developer @zcbenz on GitHub ...

Why it matters: Nvidia introduced CUDA in 2006 as a proprietary API and software layer that eventually became the key to unlocking the immense parallel computing power of GPUs. CUDA plays a major role ...

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DeepSeek-V3 represents a breakthrough in cost-effective AI development. It demonstrates how smart hardware-software co-design can deliver state-of-the-art performance without excessive costs. By ...

CUDA enables faster AI processing by allowing simultaneous calculations, giving Nvidia a market lead. Nvidia's CUDA platform is the foundation of many GPU-accelerated applications, attracting ...

Abstract: Field-Programmable Gate Arrays (FPGAs) have been extensively employed to accelerate parallel applications by allowing designers to customize their hardware for maximum performance. However, ...

Current release of ML.Net is fixed to Tensorflow 2.3.1 with CUDA and Cudnn support at 10. and 7.6 respectively. Models trained with this set up work natively with Turing based cards. Newer GPU cards ...