#Cuda

The N1X reportedly comes in two SKUs: a top-end 20-core option with 6,144 CUDA cores matching the desktop RTX 5070, and a cut-down 18-core option with 5,120 CUDA cores.…

NVIDIA CUDA 13.3 brings new capabilities and performance optimizations to developers across the CUDA ecosystem. The launch of NVIDIA CUDA Tile programming in…

Developers can now use NVIDIA CUDA Tile programming within large existing C++ GPU codebases to develop highly optimized GPU kernels using tile-based…

TL;DR After del tensor; torch.cuda.empty_cache(), PyTorch's caching allocator still...

Introduction You've written model.to('cuda') a hundred times. You've celebrated when...

From Dev Community: RTX 5090, LLaMA.cpp TurboQuant, & Blackwell CUDA Scheduling Boosts GPU Performance

A practical guide to debugging silent memory corruption in CUDA kernels, with compute-sanitizer workflows and a look at Rust-on-GPU tooling.

From Dev.to - tutorial: How to Deploy Llama 3.2 90B with vLLM + Speculative Decoding on a $16/Month DigitalOcean GPU Droplet: 2.5x Faster Inference at 1/110th Claude Cost

Nvidia's real AI moat isn't "a piece of hardware," writes Wired's Sheon Han. It's CUDA: a mature, deeply optimized software ecosystem that keeps machine-learning workloads tied to Nvidia GPUs.…

From Dev RSS Feed: RTX 5080 Launched, Rust for CUDA, & LLM GPU Scheduling Deep Dive

NVlabs publica el primer compilador oficial que convierte Rust estándar en kernels CUDA. Mismo archivo .rs para CPU y GPU, sin C++ de por medio.

There’s a deep, forbidding moat that surrounds Nvidia—and it has nothing to do with hardware.

From Dev Community: CUDA-Oxide 0.1, RTX 5070 Launch, & BeeLlama.cpp Boost 3090 Inference

From Dev.to - docker: The Zero-Trust Docker Pipeline: Securing GPU/AI Container Images from Build to Production

From Dev.to - tutorial: How to Deploy Llama 3.2 Vision with TensorRT on a $14/Month DigitalOcean GPU Droplet: 3x Faster Multimodal Inference at 1/120th Claude Vision Cost

From Dev.to - pytorch: CUDA Out of Memory at 60% Utilization: Tracing PyTorch GPU Memory Fragmentation

From Dev.to - webdev: How to Deploy Llama 3.2 11B with TensorRT-LLM on a $12/Month DigitalOcean GPU Droplet: 4x Faster Inference at 1/70th API Cost

From Dev.to - tutorial: How to Deploy Llama 3.2 405B with vLLM + Tensor Parallelism on a $40/Month DigitalOcean GPU Cluster: Enterprise-Scale Inference at 1/30th API Cost

From 3 tok/s frustration to 21 tok/s GPU-hybrid inference - a real engineer's guide to self-hosted...

A comprehensive deep-dive into AMD — latest news, products, code examples, and what it means for developers.