A developer implemented FlashAttention's forward and backward passes from scratch in pure CUDA C++, achieving O(N) memory complexity through manual SRAM tiling and online softmax recurrence. A rejecte…
KlongPy now supports a PyTorch backend that enables GPU acceleration and automatic differentiation for gradient-based computations. The torch backend outperforms NumPy by up to 8x on large arrays and …
At the ninth MLSys conference in Seattle, researchers and industry leaders focused overwhelmingly on improving the efficiency of training and deploying large language models, with specialized hardware…
The article explains that CPU-side timers in Go are unreliable for measuring GPU kernel execution time because CUDA kernel launches are asynchronous, meaning the CPU only measures the time to enqueue …
The article announces the release of SpawnDev.ILGPU.ML 4.0.0-preview.4, a C# library that runs neural networks directly in the browser using six backends (WebGPU, WebGL, WebAssembly, CUDA, OpenCL, and…
PyTorch has introduced transparent tracing and compilation for Triton kernels, allowing custom operations to be visible to the compiler for optimization. The framework now supports compiling Triton ke…
The "CUDA out of memory" error in Stable Diffusion WebUI is often caused by configuration issues rather than insufficient GPU hardware, particularly due to PyTorch's memory allocator failing to releas…
This article compares three dominant tools for local LLM inference in 2026: Ollama, llama.cpp, and vLLM. Ollama is recommended for personal, non-technical use due to its ease of setup, while llama.cpp…
A developer built a production pastebin service called mobin using only the Mojo programming language, creating 10 supporting libraries and a documentation tool from scratch in a few weeks with AI cod…
This week's top GPU news highlights three key developments: the release of Fournex, an open-source tool that analyzes Nsight Compute data to identify and suggest fixes for PyTorch/CUDA bottlenecks; a …
Detailed, step-by-step guide for building llama.cpp from source on a Dell Precision T5820 workstation equipped with an RTX 3090 Ti, achieving 42 tok/s with Qwen3.6-27B. The author emphasizes that the …
PyTorch 2.12 introduces a new device-agnostic `torch.accelerator.Graph` API that unifies graph capture and replay across CUDA, XPU, and out-of-tree backends. The release delivers up to 100x faster bat…
Ollama, an AI model server, ran on a MacBook with no NVIDIA GPU by using GTAP software to intercept CUDA calls and forward them to a remote DGX Spark workstation with a 128 GB Blackwell GPU. The setup…
The article analyzes Nvidia's GB10 processor, highlighting its integrated GPU (iGPU) based on the Blackwell architecture with 48 Streaming Multiprocessors, which is comparable to an RTX 5070 but with …
The author confesses that some previous blog posts contained unedited AI-generated segments, which compromised authenticity and hindered the development of their personal writing style. After reflecti…
The **nvidia-smi** (NVIDIA System Management Interface) is a command-line tool for monitoring, managing, and diagnosing NVIDIA GPU devices, providing data on performance, temperature, utilization, pow…
On Void Linux, an NVIDIA MX150 GPU in a ThinkPad T480 was experiencing CUDA initialization failures after sleep/wake cycles due to corrupted GPU memory mappings and MMU faults. The solution involves a…
This article provides instructions for installing the CUDA toolkit on Void Linux, detailing the use of a silent installation command with specific flags like `--override` and `--no-opengl-libs`. The a…
The article describes the author's difficulty using Valgrind for memory debugging on Artix Linux, as both Artix and Arch Linux have removed debug packages in favor of a debuginfod system. While debugi…