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For operators of large-scale AI factories, maximizing continuous output is essential for productivity. In massive-scale AI training, every GPU in the cluster...
Power is a defining constraint for AI factories. As AI workloads demand a full compute platform to serve them, each component of that platform must maximize...
Federated learning (FL) projects often begin with a straightforward setup: one server, a few clients, and one dataset at each site. As those projects grow, the...
NVIDIA reports that its Transformer Engine with JAX raises DeepSeek-V3 MoE training throughput from 103 to 1,068 TFLOPS/GPU on GB200, a 10.4x gain. The stack sustains 97% scaling efficiency at 1,024 GPUs on GB300 NVL72.
Atlas Building Composites is commercializing MIT research to turn plastic waste into parts for buildings and other infrastructure.
MIT researchers developed HardFlow, a deployment-time algorithm that lets pretrained generative models satisfy hard safety, physical, or task-specific constraints in their final output while still producing high-quality solutions, without retraining. The research appears in the IEEE Transactions on Pattern Analysis and Machine Intelligence.
If you can write down how you do your work, you can automate it. Here's what I did to support GitHub's APAC marketing team. The post Marketing ops as code: Automating events from planning to follow-up on GitHub appeared first on The GitHub Blog .
The handheld catheterization device AI-GUIDE, created by Lincoln Laboratory and Massachusetts General Hospital, promises improved health outcomes for injured service members and civilians.
Machine Intelligence
Checking agent-generated code usually means hopping between tabs. Learn how to view diffs, run terminal commands, and preview web apps side by side in the GitHub Copilot app. The post GitHub Copilot app for Beginners: Using the diff, terminal, and browser appeared first on The GitHub Blog .
NVIDIA says its NIM 2.0.12 optimized serving stack delivers up to 2.5x higher system throughput on a 4xB200 system versus a baseline without NIM optimizations, reaching 1,997 tokens per second at a 50 TPS per user target for Nemotron 3 Ultra.
Biomolecular structure prediction is now often run at proteome scale, where the goal is to move an entire worklist through the pipeline efficiently. NVIDIA...
Cloudera and Mistral join forces to bring specialized, sovereign AI intelligence to enterprise data, helping regulated industries innovate on their own terms.
NVIDIA and Palantir built a Digital Supply Chain Intelligence command center on Palantir Foundry, using NVIDIA cuOpt for weekly allocation optimization and post-training a 30B Nemotron 3.5 Lightning model on captured planner decisions. On a development benchmark, the post-trained model reached 86.7% allocation-decision accuracy.
A weeklong summer workshop brought higher education faculty to campus to explore how AI and machine learning materials can be adapted for their classrooms.
NVIDIA's developer blog describes encode-prefill-decode (EPD) disaggregation in NVIDIA Dynamo, which separates vision encoding from LLM prefill and decode for multimodal serving, reporting up to 5x faster time to first token and 7x faster end-to-end response time in tested scenarios.
Mistral helped a European energy operator migrate 40,000 lines of Fortran 77 to C++. Learn how it was done, and the lessons to carry forward.
OpenAI released GPT-6 Astra last week, and AI researcher Sebastian Raschka reports it is the best model he has used, with strong math, coding, 3D rendering, and computer-use performance. The article also examines reports that Astra uses looped transformers and hides its reasoning traces.
AlphaGenome Atlas maps the molecular effects of 9 billion single-letter DNA variants across the human genome.
Mistral today announced that it has raised €3 billion in a Series D funding round at a post-money valuation of more than €21 billion.