Reducing CUDA Binary Size to Distribute cuML on PyPI
Starting with the 25.10 release, pip-installable cuML wheels can now be downloaded directly from PyPI. No more complex installation steps or managing Conda...
NVIDIA CUDA-X Powers the New Sirius GPU Engine for DuckDB, Setting ClickBench Records
NVIDIA is partnering with the University of Wisconsin-Madison to bring GPU-accelerated analytics to DuckDB through the open-source Sirius engine. DuckDB has...
How to Train Scientific Agents with Reinforcement Learning
The scientific process can be repetitive and tedious, with researchers spending hours digging through papers, managing experiment workflows, or wrangling...
NVIDIA Kaggle Grandmasters Win Artificial General Intelligence Competition
NVIDIA researchers on Friday won a key Kaggle competition many in the field treat as a real-time pulse check on humanity’s progress toward artificial general...
Simplify GPU Programming with NVIDIA CUDA Tile in Python
The release of NVIDIA CUDA 13.1 introduces tile-based programming for GPUs, making it one of the most fundamental additions to GPU programming since CUDA was...
Focus on Your Algorithm—NVIDIA CUDA Tile Handles the Hardware
With its largest advancement since the NVIDIA CUDA platform was invented in 2006, CUDA 13.1 is launching NVIDIA CUDA Tile. This exciting innovation introduces a...
Model Quantization: Concepts, Methods, and Why It Matters
AI models are becoming increasingly complex, often exceeding the capabilities of available hardware. Quantization has emerged as a crucial technique to address...
Breaking Through Reinforcement Learning Training Limits with Scaling Rollouts in BroRL
When training large language models (LLMs) with reinforcement learning from verifiable rewards (RLVR), one of the most compelling questions is how to overcome...
Training XGBoost Models with GPU-Accelerated Polars DataFrames
One of the many strengths of the PyData ecosystem is interoperability, which enables seamlessly moving data between libraries that specialize in exploratory...
How to Achieve 4x Faster Inference for Math Problem Solving
Large language models can solve challenging math problems. However, making them work efficiently at scale requires more than a strong checkpoint. You need the...
Building an Interactive AI Agent for Lightning-Fast Machine Learning Tasks
Data scientists spend a lot of time cleaning and preparing large, unstructured datasets before analysis can begin, often requiring strong programming and...
Enhancing GPU-Accelerated Vector Search in Faiss with NVIDIA cuVS
As companies collect more unstructured data and increasingly use large language models (LLMs), they need faster and more scalable systems. Advanced tools for...
Scale Biology Transformer Models with PyTorch and NVIDIA BioNeMo Recipes
Training models with billions or trillions of parameters demands advanced parallel computing. Researchers must decide how to combine parallelism strategies,...
How to Predict Biomolecular Structures Using the OpenFold3 NIM
For decades, one of biology’s deepest mysteries was how a string of amino acids folds itself into the intricate architecture of life. Researchers built...
Introducing the CodonFM Open Model for RNA Design and Analysis
Open research is critical for driving innovation, and many breakthroughs in AI and science are achieved through open collaboration. In the field of digital...
