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 ›
Hyperparameter selection is a critical step in the deployment of modern artificial intelligence systems, given the need to tune degrees of freedom such as inference-time parameters, implementation-level settings, and thresholds driving decision rules. Despite its practical importance, hyperparameter selection is typically performed using best-effort empirical methods such as grid search or Bayesian optimization, which provide no formal statistic... 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 ›
One way is by pitting two convolutional neural networks (CNNs) against each other in a “contest” called a generative adversarial network… Read more ›
Distributed training is a technique in AI and machine learning where a model is trained using multiple computers, GPUs, or servers working… Read more ›
A step towards generalizing the transformer architecture 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 ›
Update llama first, tried with llama b9704 llama serve -m \"\.\.\gemma-4-12B-it-qat\gemma-4-12B-it-qat-UD-Q4\_K\_XL\.gguf\" --spec-type draft-mtp --spec-draft-n-max 2 --spec-draft-model \"\.\.\gemma-4-12B-it-qat\mtp-gemma-4-12B-it\.gguf\" Using MTP increases performance from 10 to 14\.8 tokens per second\. Someone recommends trying --spec-draft-n-max 3 for coding workloads Read more ›
Compiler infrastructures such as MLIR rest on a set of design principles: IR abstractions, interfaces, match-and-rewrite, flow analysis, type conversion, staged lowering, and so on. These concepts have proven themselves in practice. Good designs typically arrive through engineering knowledge, intuition and experience. Many of them, however, have correspondences in formal theory. MLIR's match-and-rewrite engine has correspondence to a \emph{term-... Read more ›
Nvidia’s European AI factory expansion also strengthens the quantum-classical stack. 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 ›
Empowering everyone to build reliable and efficient software. Read more ›
Understanding Neurons, Neural Networks, Neural Connections, Activation Functions & More Read more ›
MLflow is an open-source platform for managing the machine learning lifecycle — experiment tracking, model registry, and reproducible runs. This guide deploys MLflow using Docker Compose with a PostgreSQL backend, S3-compatible artifact storage, basic-auth, and Traefik handling automatic HTTPS, then logs a sample scikit-learn run. By the end, you'll have MLflow recording experiments at your domain over HTTPS. Prerequisite: An S3-compatible bucket (e.g. Vultr Object Storage) with access key, s... Read more ›