6 verified briefings on Gpu. Each story includes a plain-English summary, why it matters, and the concrete action engineering teams should take.
Expanse, a new YC-backed startup, has launched a tool to increase the efficiency of GPU clusters. It analyzes job scripts and code before execution to predict the actual resources needed, aiming to reduce underutilization on platforms like Kubernetes and SLURM for AI and HPC workloads.
Traditional FinOps practices often recommend downsizing resources with low utilization. However, for certain AI workloads like secure machine learning, low GPU compute usage can be misleading. These tasks may be memory-bound, not compute-bound, making "underutilized" GPUs essential for performance and avoiding higher costs.
Nvidia is discontinuing its long-standing Control Panel for GeForce GPUs with driver version 610.47. Users will now need to use newer applications like the Nvidia app for managing GPU settings. This change affects anyone managing Nvidia hardware, ending the utility's nearly two-decade run.
Nvidia is retiring its classic GeForce Control Panel after 20 years. The company is transitioning users of its Game Ready and Studio Drivers to a new, unified application, marking the end of a long-standing utility for managing GPU settings and performance.
Generative AI media platform fal has named AWS its preferred cloud provider. The partnership aims to address the significant infrastructure and compute demands required for real-time AI media generation, supporting fal's 2.5 million developer users and simplifying GPU management.
A new GitHub repository, "awesome-cuda-books," has been shared on Hacker News. It provides a curated list of books for learning CUDA, NVIDIA's parallel computing platform. The resource is aimed at developers looking to deepen their understanding of GPU programming for high-performance computing and AI applications.