Niels Cautaerts and Hossein Ghorbanfekr's book 'GPU-Accelerated Computing with Python 3 and CUDA' teaches Python developers to write GPU-accelerated code using Numba-CUDA, CuPy, RAPIDS, and JAX. The b…
Giles Thomas built a JAX training loop for an LLM from scratch, using NNX and Optax, as part of a project to replicate GPT-2 small quality without referencing Sebastian Raschka's book. He trained a ba…
The tensor4all team, led by Hiroshi Shinaoka of Saitama University, has released tenferro-rs, a Rust-native dense tensor stack for scientific computing that supports linear algebra, autodiff, einsum, …
A developer released 'flows', a custom Markdown runtime that allows users to write, run, and visualize long-running agent loops within a single .md file. The tool combines prompt blocks for fuzzy task…
The OpenXLA compiler stack and JAX now run upstream on AMD ROCm, with XLA gating every pull request on real AMD Instinct silicon through GitHub Actions and JAX running hardware tests on every ROCm PR.…
Metaflow, an open-source ML/AI orchestration framework, ranks first in every category of the Cloud Native Computing Foundation's latest Technology Radar report, with 51% of surveyed users highly likel…
PyTorch, JAX, and compiler-level tools like Enzyme use three different representations for automatic differentiation—runtime tape, functional trace-and-transform, and source (IR) transformation—each w…
Google's TPU v5p and upcoming Ironwood TPU7x offer higher raw matrix throughput and simpler interconnects than NVIDIA H100 GPUs for large-scale AI training, but GPUs remain superior for inference with…
KerasFormers, an open-source library built entirely in Keras 3, launches with over 100 transformer models spanning vision, language, multimodal, and speech, supporting seamless execution on TensorFlow…
Shrijith Venkatramana, a developer building git-lrc, explains that NVIDIA Collective Communications Library (NCCL) is the critical infrastructure enabling multi-GPU training of large language models. …
AWS released a comprehensive guide for distributed AI training on its infrastructure, featuring reference architectures, test cases, and best practices for frameworks like PyTorch, Megatron-LM, NeMo, …
A developer debugging a JAX/Flax NNX training loop discovered that the loss was stuck at 10.82, indicating the model was performing no better than random guessing. The issue was traced to the training…
JAX's default_device context manager places arrays on the specified device but does not commit them, allowing JAX to move them to other devices. This caused array lookups to take over a second by trig…
Researchers released TycoonLE, a JAX-based reinforcement learning environment for long-horizon planning in a simulated logistics economy. The environment supports action legality, delayed rewards, and…
NVIDIA has introduced the NVFP4 training recipe in TransformerEngine, enabling 4-bit mixed-precision pre-training on Blackwell GPUs with no measurable accuracy loss compared to FP8 baselines. The reci…
A developer benchmarked quantum circuit simulation backends using a 20-qubit VQE workload on an NVIDIA RTX 5090 GPU, finding JAX/XLA 12.4x faster than PyTorch and 15.7x faster than TorchQuantum for po…
JAX defaults to loading data directly onto GPU memory when a CUDA-enabled version is installed, causing out-of-memory errors for large datasets that would fit in system RAM. The framework's `jax.devic…
Google released Magenta RealTime 2, an open-weights AI model and real-time inference engine that enables live, low-latency music generation on a MacBook. The model responds instantly to MIDI, audio, a…
A developer porting PyTorch LLM code to JAX using Flax encountered difficulties when attempting to store model checkpoints with Safetensors, as the library's Flax API expects flat dictionaries but Fla…
SpaceX has nearly completed version 1.0 of a proprietary AI training stack written in C, designed to exact-map to 220,000 GB300 processors with 800G network interface cards. The system, which leverage…