Items tagged with “Training”.
Microsoft unveils plans for a 1.2 million-square-foot Fairwater AI data center in Wisconsin, housing hundreds of thousands of Nvidia GB200 GPUs. The project promises unprecedented AI training power with a closed-loop cooling system and a cost of $3.3 billion.
Reddit seeks a larger licensing deal with Google, aiming to drive more users and access to Reddit data for AI training, potentially via dynamic pricing and traffic incentives.
Explore how AWS Deep Learning Containers (DLCs) integrate with SageMaker AI managed MLflow to balance infrastructure control and robust ML governance. A TensorFlow abalone age prediction workflow demonstrates end-to-end tracking, model governance, and deployment traceability.
An addendum detailing GPT-5-Codex, a GPT-5 variant optimized for agentic coding within Codex, with safety mitigations and multi-platform availability.
AWS introduces topology-aware scheduling with SageMaker HyperPod task governance to optimize training efficiency and network latency on EKS clusters, using EC2 topology data to guide job placement.
Explores quantization aware training (QAT) and distillation (QAD) as methods to recover accuracy in low-precision models, leveraging NVIDIA's TensorRT Model Optimizer and FP8/NVFP4/MXFP4 formats.
NVIDIA Spectrum-XGS Ethernet enables scale-across networking to unify dispersed data centers into a single AI factory, delivering significant NCCL bandwidth gains and predictable performance over long distances.
OpenAI launches a Jobs Platform and new Certifications to connect workers with AI-driven jobs, training, and credentials, backed by partnerships across business and government.
A detailed look at using the HyperPod CLI and SDK to streamline training and deploying large AI models on SageMaker HyperPod, with practical steps for FSDP-based training and inference deployment.
SageMaker HyperPod introduces a one-click, validated cluster creation experience that automatically provisions prerequisite resources and prescriptive defaults, accelerating distributed training and inference with Slurm or EKS orchestration.
NVIDIA outlines a SFT + QAT workflow to recover FP4 accuracy for gpt-oss fine-tuning, comparing MXFP4 and NVFP4, and detailing deployment and performance gains with 98% pass rates on select tasks.
SageMaker HyperPod now offers managed node automatic scaling powered by Karpenter, enabling responsive, cost-aware scaling for inference and training workloads on HyperPod clusters.
Anthropic will start training its AI models on user chats and coding sessions unless users opt out, extending data retention to five years for non-opt-out accounts. The deadline is September 28, 2025, with updates applying to Claude consumer tiers only.
A production-ready, modular telesurgery workflow from NVIDIA Isaac for Healthcare unifies simulation and clinical deployment across a low-latency, three-computer architecture. It covers video/sensor streaming, robot control, haptics, and simulation to support training and remote procedures.
Anthropic has reached a proposed class settlement with US authors over claims it trained Claude AI on pirated works, allowing the case to skip a trial. Terms are not fully disclosed as finalization is expected by September 3.
OpenAI announces the Learning Accelerator to bring advanced AI to India's educators and millions of learners through accelerated AI research, training, and deployment.
NVFP4 is a 4-bit data format delivering FP16-level accuracy with the throughput and memory efficiency of 4-bit precision, extended to pretraining for large language models. This profile covers 12B-scale experiments, stability, and industry collaborations.
SageMaker HyperPod adds continuous provisioning and custom AMIs to deliver flexible resource provisioning, security-aligned environments, and enterprise-grade control for large-scale foundation model training and inference on EKS.
NVIDIA Blackwell Ultra accelerates AI training and inference with dual-die integration, NVFP4 4-bit precision, 288 GB HBM3e, and high-bandwidth interconnects, enabling scalable AI factories at scale.
Apple ML Research introduces SlowFast-LLaVA-1.5 (SF-LLaVA-1.5), a family of token-efficient video LLMs designed for long-form video understanding. It leverages SlowFast two-streams and public data to achieve state-of-the-art results at 1B–7B scales, with mobile-friendly implications.
An in‑depth profile of NVIDIA Blackwell Ultra, its dual‑die NV‑HBI design, NVFP4 precision, 288 GB HBM3e per GPU, and system‑level interconnects powering AI factories and large‑scale inference.
David Luan, head of Amazon's AGI Lab, argues that solving AI agents is the next major frontier, outlining a factory-like approach to building smarter models and stressing real-world task completion beyond chat.
A detailed look at fine-tuning GPT-OSS models with SageMaker HyperPod recipes and training jobs, including multilingual data, deployment, and practical steps for enterprise-grade training.
A Microsoft Research podcast recap examining how AI is reshaping healthcare economics, biomedical research, and medical education, with a focus on navigating medical education in the era of generative AI.
Learn how Code Editor and multiple spaces in Amazon SageMaker Unified Studio accelerate end-to-end ML workflows, from development to training, evaluation, and optional deployment.
NVIDIA NeMo-RL v0.3 adds Megatron-Core support to boost training throughput for large models, addressing DTensor limitations, with long-context training, MoE support, and simplified configuration.
Overview of NeMo-RL v0.3 with Megatron-Core backend for post-training large models, detailing 6D/4D parallelism, GPU-optimized kernels, and simplified configuration to boost reinforcement learning throughput on models at scale.
The Nemotron Nano 2 9B is an open enterprise‑grade reasoning model with a Hybrid Transformer–Mamba backbone and a configurable thinking budget, delivering leading accuracy and 6x throughput for edge and hybrid deployments.
In this post, we explore how to design and implement custom plugins for Amazon Q Business to create an intelligent chatbot that streamlines employee training by retrieving answers from training materials. The solution implements secure API access using Amazon Cognito for user authentication and auth
Open Nemotron Nano 2 9B delivers leading accuracy and up to 6x throughput with a Hybrid Transformer–Mamba backbone and a configurable thinking budget, aimed at edge, PC and enterprise AI agents.
NVIDIA’s co-packaged optics (CPO) approach delivers dramatic power efficiency for large-scale AI data centers, with Quantum-X Photonics and Spectrum-X Photonics enabling high-bandwidth, low-latency networking.
OCAT fine-tunes a CAT model by freezing most parameters and tuning a small corpus-related set, delivering stronger translation quality on WMT23 EN→ZH and EN→DE while maintaining robustness to hyperparameters.
Apple Research shows a joint ASR and pitch accent detection model enhances semi-supervised speech representations, achieving a 41% improvement in F1-score for pitch accent detection and a 28.3% WER reduction on LibriSpeech under limited-resource fine-tuning.
Dion is a new AI model optimization method that boosts scalability and performance by orthonormalizing only a top-rank subset of singular vectors, enabling more efficient training of large models like LLaMA-3 with reduced overhead.
NVIDIA's Omniverse NuRec pairs 3DGUT with COLMAP to turn real-world data into photorealistic USD assets for Isaac Sim and CARLA, accelerating sim-to-real testing and robot training.
NVIDIA Research explores World Foundation Models (WFMs) for synthetic data generation and data curation to accelerate robot and industrial AI training, detailing Cosmos Predict, Cosmos Transfer, and Cosmos Reason and related workflows.
Overview of Accelerate's ND‑Parallel capabilities: combining data, tensor, sharded and context parallelism with Axolotl to scale large-model training across multi‑GPU and multi‑node environments.
A concise resource on composing multiple parallelism strategies (DP, FSDP, TP, CP) with Accelerate and Axolotl to train large models across many GPUs, with guidance on configuration, use cases, and trade‑offs.
OpenAI debuts safe-completions in GPT-5, a nuanced safety-training approach that aims to maximize helpfulness within safety boundaries, especially for dual-use prompts.
XGBoost 3.0 enables terabyte-scale GBDT training on a single GH200 Grace Hopper Superchip, streaming data from host RAM via external-memory Quantile DMatrix for faster, simpler scaling.
TRL expands multimodal alignment for Vision-Language Models with GRPO, GSPO, MPO, and native SFT support, plus ready-to-run scripts and notebooks to streamline post-training alignment.
Overview of TRL's multimodal alignment methods for vision-language models: GRPO, GSPO, MPO, plus native SFT support and DPO-based baselines.
Open-source, free Python library for experiment tracking with a local Gradio dashboard that can sync to Hugging Face Spaces for easy sharing and collaboration. Drop-in replacement for wandb with a lightweight beta design.
Explores how generative AI is transforming medical education, with insights from Morgan Cheatham and Daniel Chen on tool integration and the questions of trust, training, and responsibility.
Meta details its approach to maintaining reliability in its AI hardware at scale, covering fault categories, detection of silent data corruptions, and strategies that keep large training clusters like Llama 3 operational.
Overview of Arc Institute's Virtual Cell Challenge, its STATE baseline, and the implications for in silico cell perturbation modeling and context generalization in transcriptomics.
Ettin introduces the first state-of-the-art paired encoder-only and decoder-only models trained with identical data and recipes, measuring apples-to-apples performance across tasks and scales.
PLAID jointly generates protein 1D sequences and 3D structures by learning the latent space of protein folding models. It enables function- and organism-guided prompts and decodes structure with frozen folding-model weights.
100 RL-controlled cars deployed on I-24 during rush hour to dampen stop-and-go waves, improve throughput, and reduce fuel use for all road users. Decentralized controllers rely on basic radar sensors and local observations.
Explains world models (world simulators), their origins, current developments like Sora, potential applications, challenges, and implications for developers and enterprises.
As progress in ML shifts toward scale and empiricism, mathematics is evolving from strict guarantees to post-hoc explanations, higher-level structure, and symmetry-aware design, spanning topology, geometry, and algebra to interpret and guide large-scale models.
Explores how mathematics remains central to ML, but its role is evolving from theory-first guarantees to geometry, symmetries, and post-hoc explanations in scale-driven AI.
Analysis of how ChatGPT responds to different English dialects, highlighting biases against non-standard varieties and implications for global users.
Explores why purposeful, multi round dialogue matters for LLM chatbots beyond one shot prompts, and outlines training, evaluation, and implementation challenges for engineers and enterprises.
A pragmatic argument to ground AI development in wellbeing science, outlining what beneficial AI could mean in practice and concrete leverage points for researchers and product teams.
A new MIQA benchmark tests Large Multimodal Models on visual retrieval and reasoning across 1–10K images, revealing key limitations and introducing MIRAGE, a single-stage approach to scale LMMs.
A study demonstrating how small language models can perform accurate function calling at the edge using a curated data pipeline, an LLMCompiler-based planner, and on-device execution with macOS integrations.
An in-depth look at how large language models (LLMs) relate to financial markets: token counts, predictability limits, multimodal approaches, synthetic data, residualization, and practical implications for quant and fundamental work.
A practical guide to why models that look good in testing fail in the real world — covering misleading data, hidden variables, spurious correlations, mislabelling, data leakage, and concrete checks to avoid them.
An in-depth look at common reasons machine learning models fail in real-world data, with practical guidance and a path to safer, more reliable evaluation.
Explores how graph neural networks can learn to execute classical algorithms through algorithmic alignment, improving reliability, generalisation, and out-of-distribution performance.