Rust-GPU compiles (embedded) Rust to GPU shaders. You can then use these shaders in the bevy game engine, your custom wgpu render or whatever else needs shaders. We'll look at how it compares to DSLs for GPU programming, such as glsl, wgsl or burn. And why are we targetting SPIR-V, yet can compile to wgsl and run on the web. And if we really can compile ordinary rust, what could we run on graphics cards? Licensed to the public under about this event: Read more ›
Learn how to use DigitalOcean’s Inference Router to govern multi-model API costs, route requests by task complexity, and reduce LLM inference spend. Read more ›
This is Day 11 of building a neural network from scratch. Yesterday we went over gradient descent: read the slope of the loss at your… Read more ›
Learn Axum fundamentals to build Rust web apps without sacrificing development speed. Read more ›
Learn if PyTorch models can leverage XLA to boost model training on GPUs. Explore how XLA integration works in PyTorch for GPUs and TPUs. Read more ›
Track ML experiments with MLflow in under 10 minutes — log params, metrics, and models in 3 lines of Python. Real benchmarks on sklearn and PyTorch. Read more ›
New token-level analyses of Olmo 3 and Olmo Hybrid show that hybrid models predict meaning-bearing, context-dependent tokens better than transformers, while transformers retain an edge on verbatim copying. Read more ›
Evolutionary hyperparameter tuning and feature selection for scikit-learn Read more ›
OpenPangu models are attractive targets for private and domestic large-language-model deployment, yet their robustness under aggressive post-training quantization on Ascend NPUs has not been systematically characterized. This paper conducts a controlled empirical study of OpenPangu 1B and 7B models on Huawei Ascend 910B1 NPUs. We evaluate representative weight-only and weight-activation post-training quantization methods, including RTN, GPTQ, ... Read more ›
This post shows you how to configure training jobs on Amazon SageMaker AI to get the most out of Blackwell’s architecture on AWS. You learn how to select batch sizes and sequence lengths that take advantage of Blackwell’s expanded memory, choose the right precision format for your model size (1B to 64B parameters), and apply activation checkpointing strategically. By the end, you have a practical framework for tuning your training configuration and launching distributed training jobs on P6-B2... Read more ›
A bare-metal C++ AI proxy that predicts prompt complexity in 4.59 milliseconds and dynamically routes traffic to the most cost-effective LLM. Read more ›
One way is by pitting two convolutional neural networks (CNNs) against each other in a “contest” called a generative adversarial network… Read more ›
BoxLang 1.14.0 ships a lot of exciting features -- Dynamic Sets, Ranges, Inner Classes, JSONPath navigation -- but one quietly powerful addition will change the way you think about every database call in your application: Query Transformers, and this is ... The post appeared first on <a href=" Read more ›
Welcome to the six hundred and fifty-first issue of LLVM Weekly, a weekly newsletter (published every Monday) covering developments in LLVM, Clang, and related projects. LLVM Weekly is brought to you by Alex Bradbury. Subscribe to future issues at and pass it on to anyone else you think may be interested. Please send any tips or feedback via email: asb@asbradbury.org, or Mastodon: @llvmweekly@fosstodon.org / @asb@fosstodon.org, or Bluesky: @llvmweekly.org / @asbradbury.org. Read more ›
Qualcomm is acquiring Modular, the AI software startup behind the Mojo programming language and MAX inference engine, for $3.9 billion in stock. The deal gives Qualcomm the software layer to run AI on its own chips, challenging Nvidia's CUDA ecosystem that has locked in developers for 17 years. Read more ›
Linaro Forge 26.0 introduces NCCL collective profiling in MAP and Performance Reports, giving full visibility into GPU-to-GPU communication at scale. We put it to the test on a multi-node cluster, read this blog and see what we found, with zero code changes required. Read more ›
Community-driven benchmark suite for MLX inference engines on Apple Silicon - igurss/mlx-chronos Read more ›
arXiv:2311.16707v2 Announce Type: replace-cross Abstract: Dense prediction is a fundamental requirement for many medical vision tasks such as medical image restoration, registration, and segmentation. The most popular vision model, Convolutional Neural Networks (CNNs), has reached bottlenecks due to the intrinsic locality of convolution operations. Recently, transformers have been widely adopted for dense prediction for their capability to capture long-range visual dependence. However, due to... Read more ›
Empowering everyone to build reliable and efficient software. Read more ›
Understanding Neurons, Neural Networks, Neural Connections, Activation Functions & More Read more ›