CUDA Tile is a new Python package that simplifies GPU programming by automatically tiling loops, handling data transfer, and optimizing memory access. It allows developers to write concise and readable code that leverages the full power of NVIDIA GPUs without needing to manually manage the complexities of parallel programming.
A new patch enables Nvidia GPU support on Raspberry Pi 5 and Rockchip devices, allowing for GPU-accelerated compute tasks. The article details the setup process, performance testing with llama.cpp, and current limitations with display output.
This article details the integration of Docker Model Runner with the NVIDIA DGX Spark, enabling faster and simpler local AI model development. It covers setup, usage, and benefits like data privacy, offline availability, and ease of customization.
Nvidia's DGX Spark is a relatively affordable AI workstation that prioritizes capacity over raw speed, enabling it to run models that consumer GPUs cannot. It features 128GB of memory and is based on the Blackwell architecture.
Nvidia introduces the Rubin CPX GPU, designed to accelerate AI inference by decoupling the context and generation phases. It utilizes GDDR7 memory for lower cost and power consumption, aiming to redefine AI infrastructure.
Nvidia has expanded its Jetson lineup with the Jetson AGX Thor Developer Kit, a compact platform that carries the new Jetson T5000 system-on-module. Marketed as a developer system, the dimensions and form factor place it firmly in the realm of a mini PC, although its design and purpose align more with edge AI deployment than home computing.
Running GenAI models is easy. Scaling them to thousands of users, not so much. This guide details avenues for scaling AI workloads from proofs of concept to production-ready deployments, covering API integration, on-prem deployment considerations, hardware requirements, and tools like vLLM and Nvidia NIMs.
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.
Learn how GPU acceleration can significantly speed up JSON processing in Apache Spark, reducing runtime and costs for enterprise data applications.
