GitHub - raphamorim/rio: A hardware-accelerated GPU terminal emulator focusing to run in desktops and browsers.You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Reload to refresh your session. You switched accounts on another tab or window. Reload to refresh your session. Dismiss alert
Unlocking Efficient AI: zymtrace distributed GPU Profiler, now publicly available - zymtrace
Unlocking Efficient AI: zymtrace distributed GPU Profiler, now publicly available Identify performance bottlenecks in CUDA kernels, optimize inference batch size, and eliminate idle GPU cycles —with zero friction. GPUs are essential for training and inference at scale. Organizations are investing millions into GPU clusters—not just in hardware acquisition, but also in the electricity required to p
Rust GPU The future of GPU programming
use glam::UVec3; use spirv_std::spirv; enum Outcome { Fizz, Buzz, FizzBuzz, } trait Game { fn fizzbuzz(&self) -> Option<Outcome>; } impl Game for u32 { fn fizzbuzz(&self) -> Option<Outcome> { match (self % 3 == 0, self % 5 == 0) { (true, true) => Some(Outcome::FizzBuzz), (true, false) => Some(Outcome::Fizz), (false, true) => Some(Outcome::Buzz), _ => None, } } } #[spirv(compute(threads(64)))] pub
GitHub - philipturner/metal-benchmarks: Apple GPU microarchitecture
This document thoroughly explains the M1 and M2 GPU architectures, focusing on GPGPU performance. Details include latencies for each ALU assembly instruction, cache sizes, and the number of unique instruction pipelines. This document enables evidence-based reasoning about performance on the Apple GPU, helping people diagnose bottlenecks in real-world software. It also compares Apple silicon to gen
Host your LLMs on Cloud Run | Google Cloud Blog
Run your AI inference applications on Cloud Run with NVIDIA GPUs Developers love Cloud Run for its simplicity, fast autoscaling, scale-to-zero capabilities, and pay-per-use pricing. Those same benefits come into play for real-time inference apps serving open gen AI models. That's why today, we’re adding support for NVIDIA L4 GPUs to Cloud Run, in preview. This opens the door to many new use cases
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ç±³NVIDIAã¯7月17日（ç¾åœ°æ™‚間）ã€ã€ŒNVIDIA Transitions Fully Towards Open-Source GPU Kernel Modulesã€ã¨é¡Œã—ãŸãƒ–ãƒã‚°è¨˜äº‹ã‚’公開ã—ã€GPUã®ã‚«ãƒ¼ãƒãƒ«ãƒ‰ãƒ©ã‚¤ãƒãƒ¢ã‚¸ãƒ¥ãƒ¼ãƒ«ã‚’完全ã«ã‚ªãƒ¼ãƒ—ンソース化ã—ã¦ã„ãã¨ç™ºè¡¨ã—ãŸã€‚ NVIDIAãŒæ–¹é‡è»¢æ›ã€ã‚«ãƒ¼ãƒãƒ«ãƒ‰ãƒ©ã‚¤ãƒãƒ¢ã‚¸ãƒ¥ãƒ¼ãƒ«ã‚’オープンソース化㸠NVIDIAã¯2022å¹´ã«ã‚‚Linuxå‘ã‘ã®GPUカーãƒãƒ«ãƒ¢ã‚¸ãƒ¥ãƒ¼ãƒ«ï¼ˆãƒãƒ¼ã‚¸ãƒ§ãƒ³R515）をオープンソースã¨ã—ã¦ãƒªãƒªãƒ¼ã‚¹ã—ã¦ãŠã‚Šã€ãƒ‡ãƒ¼ã‚¿ã‚»ãƒ³ã‚¿ãƒ¼å‘ã‘ã®ä¸€éƒ¨GPUã§åˆ©ç”¨ã§ããŸã€‚ã—ã‹ã—今回ã€R560をベースã«ã—ãŸæœ€æ–°ãƒ‰ãƒ©ã‚¤ãƒã®ãƒªãƒªãƒ¼ã‚¹ã‹ã‚‰ã‚ªãƒ¼ãƒ—ンソースGPUカーãƒãƒ«ãƒ¢ã‚¸ãƒ¥ãƒ¼ãƒ«ã¸ã®å®Œå…¨ç§»è¡Œã—ã¦ã„ãã¨ç™ºè¡¨ã€‚テストã§ã¯ã‚¯ãƒãƒ¼ã‚ºãƒ‰ã‚½ãƒ¼ã‚¹ã®ãƒ‰ãƒ©ã‚¤ãƒã¨åŒç‰ä»¥ä¸Šã®ãƒ‘フォーマンスを発æ®ã§ããŸã¨ã—ã¦ãŠã‚Šã€NVIDIAã¯éšæ™‚オープンソースã®GPUカーãƒãƒ«ãƒ¢ã‚¸ãƒ¥ãƒ¼ãƒ«ã«åˆ‡ã‚Š
NVIDIA to Acquire GPU Orchestration Software Provider Run:ai
Israeli startup promotes efficient cluster resource utilization for AI workloads across shared accelerated computing infrastructure. To help customers make more efficient use of their AI computing resources, NVIDIA today announced it has entered into a definitive agreement to acquire Run:ai, a Kubernetes-based workload management and orchestration software provider. Customer AI deployments are bec
Intro
infoThis documentation is a work-in-progress. Use.GPU is in alpha. warning_amberWebGPU is only available in certain browsers. Use.GPU is a set of declarative, reactive WebGPU legos. Compose live graphs, layouts, meshes and shaders, on the fly. It's a stand-alone Typescript+Rust/WASM library with its own React-like run-time. If you're familiar with React, you will feel right at home. It has a built
NVIDIA Releases Open-Source GPU Kernel Modules | NVIDIA Technical Blog
NVIDIA has released its Linux GPU kernel modules as open source with a dual GPL/MIT license, starting with the R515 driver release, to improve the experience of using NVIDIA GPUs in Linux.The open-source kernel modules are production-ready for data center GPUs in the NVIDIA Turing and NVIDIA Ampere architecture families, while support for GeForce and Workstation GPUs is alpha-quality.The release i
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GitHub - sirius-db/sirius
GitHub - VictorTaelin/WebMonkeys: Massively parallel GPU programming on JavaScript, simple and clean.
GitHub - tracel-ai/cubecl: Multi-platform high-performance compute language extension for Rust.
What's next for WebGPU  | Blog  | Chrome for Developers
OpenAI will start using AMD chips and could make its own AI hardware in 2026
GitHub - Rust-GPU/rust-gpu: 🉠Making Rust a first-class language and ecosystem for GPU shaders 🚧
DigitalOcean GPU Droplets, scalable computing power on demand | DigitalOcean
Nvidia's flagship gaming GPU can crack complex passwords in under an hour
https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=802fed876e180f32c06ede4d1cc951ba44f79dd2
GitHub - NVIDIA/open-gpu-kernel-modules: NVIDIA Linux open GPU kernel module source
Intel beats AMD and Nvidia to become the first with full AV1 support with its new Arc GPUs
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