NVIDIA DGX Spark is a desktop-friendly AI supercomputer powered by the NVIDIA GB10 Grace Blackwell Superchip, delivering 1000 AI TOPS of performance with 128GB of memory. It is designed for prototyping, fine-tuning, and inference of large AI models.
NVIDIA's Project Aether automates the qualification, testing, configuration, and optimization of Spark workloads for GPU acceleration, enabling enterprises to process data more efficiently and cost-effectively.
Unify your existing devices into one powerful GPU: iPhone, iPad, Android, Mac, NVIDIA, Raspberry Pi, pretty much any device!
The Hat uPCIty Lite is a PCI Express evaluation board with an open-ended PCIe X4 slot, designed for the Raspberry Pi 5. It supports external power, isolates PCIe Express power delivery to protect the Pi, and is compatible with PCIe x1 interface in Gen2 and Gen3 standards. The board includes all necessary accessories and is built with high-quality components.
Learn how GPU acceleration can significantly speed up JSON processing in Apache Spark, reducing runtime and costs for enterprise data applications.
Learn how to use a spare GPU to create an external graphics card (eGPU) for your laptop or PC gaming handheld, including using prebuilt enclosures, DIY Thunderbolt enclosures, or OCuLink enclosures.
The article discusses the competition Nvidia faces from Intel and AMD in the GPU market. While these competitors have introduced new accelerators that match or surpass Nvidia's offerings in terms of memory capacity, performance, and price, Nvidia maintains a strong advantage through its CUDA software ecosystem. CUDA has been a significant barrier for developers switching to alternative hardware due to the effort required to port and optimize existing code. However, both Intel and AMD have developed tools to ease this transition, like AMD's HIPIFY and Intel's SYCL. Despite these efforts, the article notes that the majority of developers now write higher-level code using frameworks like PyTorch, which can run on different hardware with varying levels of support and performance. This shift towards higher-level programming languages has reduced the impact of Nvidia's CUDA moat, though challenges still exist in ensuring compatibility and performance across different hardware platforms.
The article discusses the challenges and strategies for load testing and infrastructure decisions when self-hosting Large Language Models (LLMs).
The US Commerce Department has proposed new rules requiring developers of large AI models and those providing the infrastructure to train them to report details about their operations. This is in response to concerns about the potential risks posed by advanced AI, including its potential use in cybercrime and the development of weapons.
Run:ai offers a platform to accelerate AI development, optimize GPU utilization, and manage AI workloads. It is designed for GPUs, offers CLI & GUI interfaces, and supports various AI tools & frameworks.
