#CUDA
10 related articles
NVIDIA CUDA 13.5 Released: Official Support for Vera Rubin
NVIDIA releases CUDA 13.5, with the core change being GA support for the Vera Rubin platform. This article explains what the release means for developers, the context of platform alignment, and key points to watch when adapting.
CUDA 13.4 Memory Enforcement: A New Solution for Kubernetes GPU Sharing
NVIDIA CUDA 13.4 introduces memory enforcement, moving Kubernetes GPU sharing from scheduling conventions to runtime hard constraints. This article analyzes how it works with the K8s resource model, applicable scenarios, and quota granularity and compute contention issues during implementation.
Hands-On GPU Kernel Development Environment Setup: CUDA 13 Thresholds and Performance Analysis Pitfalls
Based on a Reddit developer's notes, this article covers three traps in GPU kernel dev environment setup: CUDA 13 GPU architecture support thresholds, timing errors under asynchronous execution, and profiler counter permission issues, with engineering advice for troubleshooting and avoidance.
vLLM v0.33.0rc0 Released: CUDA Profiling Fix and MRV2 Optimization
vLLM releases v0.33.0rc0, fixing MRV2 skipping adaptive verification cost profiling in CUDA. This article explains the bugfix, its impact on inference deployment, and upgrade notes.
Ollama v0.40.7-rc1: MLX and GGML Share CUDA Runtime, Slimmer Package Size
Ollama v0.40.7-rc1 deduplicates the CUDA runtime shared by MLX and GGML, moving it into a sibling CUDA payload directory, retaining MLX private libs and JIT headers, packaging the full OpenBLAS Fortran runtime closure, and adding dependency checks at release to shrink the package and simplify dependency resolution.
One-Click AI PC Setup: Hands-On with the Windows and Ubuntu AI Environment Installers
SECourses releases one-click AI environment installers for Windows and Ubuntu, auto-configuring CUDA 12.9/13.2, cuDNN, Python 3.10-3.14, and the C++ toolchain, verified by 78 and 66 real tests.
NVIDIA Green Contexts: Fine-Grained Control Over GPU Resource Allocation
NVIDIA Green Contexts lets developers programmatically partition GPU SM resources within a single process, isolating latency-sensitive and throughput-intensive workloads for predictable performance.
Product ReviewsNVIDIA CompileIQ: Auto-Tuning Compiler Options to Squeeze Every Drop of GPU Kernel Performance
Deep dive into NVIDIA CompileIQ auto-tuning tool that intelligently searches compiler option combinations to find optimal build parameters for CUDA kernels.
TutorialsNVIDIA CUDA Tile: A Detailed Look at the New Tile-Based GPU Programming Paradigm
In-depth analysis of NVIDIA's new CUDA Tile programming model: how tile-based GPU kernel development integrates with C++, optimizes data locality, and accelerates Tensor Core computation for HPC, deep learning, and AI inference.
TutorialsNCCL Multi-GPU Communication Optimization in Practice: A Guide to Scaling HPC High-Performance Computing
Deep dive into NVIDIA NCCL multi-GPU communication library principles and optimization strategies, covering AllReduce, NVLink, and GPUDirect RDMA to help HPC and AI developers master scaling from single-node to massive clusters.