#GPU is an active hashtag on Bluesky. In the last 30 days, 135 people shared 385 posts with it — around 13 a day. Activity is up 17% versus the previous week, peaking on Jul 21 with 27 posts.
Tags most often used together with #GPU.
gpuopen.com
GPU view-adaptive crack-free subdivision of Bézier surfaces - AMD GPUOpen
Learn how fast, crack-free GPU work graph subdivision for bicubic Bézier surfaces dramatically reduce triangle counts while simplifying implementation and matching hardware-tessell
www.gamingonlinux.com
Intel announced their Arc B-Series GPUs and XeSS 2, the day after CEO Pat Gelsinger retires
A bit of a double-whammy this week from Intel. Not only did they announce that CEO Pat Gelsinger has retired, but they've also now properly revealed their new Arc B-Series GPUs.
www.collabora.com
Building Tyr in Rust: CSF architecture and booting the MCU
How Tyr maps Vulkan submissions onto Mali CSF groups, queues, firmware interfaces, and GPU execution.
www.gamingonlinux.com
Linux GPU Configuration Tool (LACT) upgrades NVIDIA clockspeed configuration
If you want to fully control your GPU on Linux you may want to check out the latest update to Linux GPU Configuration Tool (LACT), which brings in various new features.
www.youtube.com
EGSR 2026: GPU View-Adaptive Crack-Free Subdivision of Bézier Surfaces
YouTube video by EGSR 2026
cloud.google.com
Advancing brain tumor research with privacy-first AI
The intersection of medicine and artificial intelligence has led to numerous innovations, but developers are now facing the challenge of building robust medical AI tools that have been tested and evaluated on diverse, real-world patient data while protecting patient privacy. To address this issue, Google Cloud is collaborating with MLCommons through the MedPerf initiative, which uses Confidential Computing to establish a secure environment for benchmarking AI models. The MedPerf initiative, launched by MLCommons in 2023, aims to standardize the evaluation of medical AI by using federated evaluation to test models. By utilizing Google Cloud Confidential Space, proprietary AI models can be evaluated inside hardware-isolated Trusted Execution Environments, ensuring that none of the parties involved can see model code or patient data. The Confidential VM extends beyond the CPU to the GPU, protecting model weights and patient data during GPU-accelerated inference. The MedPerf platform is already driving critical research, such as the Federated Tumor Segmentation initiative, which is working to improve brain tumor research by validating AI models on private brain MRI data from around the world. This collaborative approach demonstrates that AI tools can be proven to work across a truly representative patient population, achieving clinical trust and validation. The impact of this collaboration is significant, with researchers and clinicians praising the secure, scalable, and collaborative cloud environment provided by Google Cloud. The future of medical AI holds enormous promise, but it can only be realized if clinicians, researchers, and regulators can trust the benchmarks used to evaluate it. By making it easier to securely share and evaluate data and models, the collaboration between MLCommons and Google Cloud is clearing the path for faster, safer, and more equitable medical breakthroughs.
www.ebay.com
MSI Gaming X Trio GeForce RTX 3090 24GB - Graphics Card - Excellent Condition
Certified refurb · 1499.99 — track it on DealKestrel
gpuopen.com
GPU view-adaptive crack-free subdivision of Bézier surfaces - AMD GPUOpen
Learn how fast, crack-free GPU work graph subdivision for bicubic Bézier surfaces dramatically reduce triangle counts while simplifying implementation and matching hardware-tessell
cloud.google.com
Mirendil taps AI Hypercomputer TPUs and GPUs for pre- and post-training applications
cloud.google.com
Mirendil taps AI Hypercomputer TPUs and GPUs for pre- and post-training applications
Google Cloud infrastructure is the preferred platform for most major AI labs and high-growth AI startups. These organizations rely on it for training models, inference, and cutting-edge research. Mirendil, a new frontier AI lab, is now also leveraging Google Cloud's AI Hypercomputer. This will involve using a combination of Google's TPU AI accelerators and NVIDIA's AI infrastructure. This specialized setup is designed to support Mirendil's model pre-training and post-training applications. Mirendil aims to develop AI systems that accelerate and democratize AI research and development. Their work involves managing complex training workflows and powering large-scale reinforcement learning. The flexibility to choose both TPUs and NVIDIA's platform on Google Cloud provides Mirendil with rapid access to compute resources. This allows them to match workloads to the most suitable architecture over time. Google Cloud partnered closely with Mirendil on the design and deployment of this integrated infrastructure. They also collaborated on a system using managed training clusters within the Gemini Enterprise Agent Platform. Mirendil is already utilizing TPU v5P chips and will soon integrate NVIDIA accelerated computing systems. This expansion allows Mirendil to scale and enhance their AI systems. They aim to put advanced AI research capabilities into the hands of more scientists and engineers.
hackernoon.com
62 Blog Posts To Learn About Gpu
www.youtube.com
EGSR 2026: GPU View-Adaptive Crack-Free Subdivision of Bézier Surfaces
YouTube video by EGSR 2026
www.ebay.com
Alienware X16 R2 480Hz FHD+ 2.3 GHz Ultra 9 185H 32GB 2TB SSD NVIDIA RTX 4090
Certified refurb · 2199.99 — track it on DealKestrel
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