First look at SPARTA, a distributed AI training algorithm... that avoids synchronization by randomly exchanging sparse sets of parameters ( 1,000x reduction in inter-GPU communication, enabling training of large models over slow bandwidths without specialized infrastructure. SPARTA works on its own but can also be combined with sync-based low communication training algorithms like DiLoCo for even better performance.show more

ChatGPT uses your data for training its Large Language... Model (LLM). But now you can ask ChatGPT to NOT use data for training its model. Go to "Settings." Click on "Show" in front of "Data Controls." Turn off "Chat History and Training."show more

Decentralized Diffusion Models power stronger models trained on more... accessible infrastructure. DDMs mitigate the networking bottleneck that locks training into expensive and power-hungry centralized clusters. They scale gracefully to billions of parameters and generate photorealistic images with just a week of training on eight independent GPU nodes. They’re easy to implement, adopt DiT hyperparameters directly and outperform standard models FLOP-for-FLOP.show more

We are excited to announce our strategic partnership with... Fraction AI for their IIO launch. Fraction AI is the first decentralized training platform for AI agents with millions of sessions, 150,000+ agents deployed, and over $1M in rewards distributed. On our platform, thousands of AI agents compete in specialized domains and continuously improve through Reinforcement Learning. Stay tuned for upcoming surprise announcements.show more

🚀Thrilled to share what we’ve been building at TRI... over the past several months: our first Large Behavior Models (LBMs) are here! I’m proud to have been a core contributor to the multi-task policy learning and post-training efforts. At TRI, we’ve been researching how LBMs can help robots learn faster, better, and more efficiently. The key takeaways: ✅ We built an evaluation pipeline to benchmark LBM performance with real 𝐬𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐚𝐥 𝐜𝐨𝐧𝐟𝐢𝐝𝐞𝐧𝐜𝐞 ✅ Pre-training on hundreds of tasks makes models more robust—plus, we can teach new, complex tasks with 80% 𝐥𝐞𝐬𝐬 𝐝𝐚𝐭𝐚 ✅ The bigger and more diverse the pre-training, the better the results Check out our overview video, webpage and paper for more details: ✨ 🌎 📄 We hope this work helps move the field of robotics forward!show more

Grok 4.5, with 1.5 T parameters, will be three... times as large as Grok 4.3. Grok 5 is training on even larger models with 6 and 10 trillion parameters. Both are expected to be released later this year.show more

⚙️ Decentralized AI Training Powerhouse VerAI leverages decentralized compute... to deliver efficient, scalable, and sustainable AI training for developers. 🌍 Our platform is built to optimize performance across diverse workloads, empowering you to build cutting-edge AI solutions. Here’s how VerAI drives technical excellence: 💻 VerAI’s Technical Edge ✅P2P Compute Network: Dynamically orchestrates AI training across consumer GPUs, cutting costs by 50% through efficient resource sharing. ✅API-Driven Integration: Supports TensorFlow, PyTorch, and more via robust APIs, enabling seamless multi-framework workflows. ✅Workload Optimization: Advanced scheduling ensures efficient resource allocation for training large models like LLMs. ✅Sustainable Compute: Reduces CO2 emissions by 20% per cycle with shared resources, aligning AI with eco-friendly practices. Join our waitlist and unlock the power of decentralized AI training optimized for performance, cost, and sustainability! 🌱 👉 #VerAI #AIForAll #SustainableTech #JoinTheFuture #EarnCrypto #AIAgents #Innovationshow more

Why Memory and GPUs Have to Be Co-Located “If... you want to do large-scale training like we do, you need the memory to be co-located with a large cluster of GPUs. I can’t just rent from Google, Microsoft, or even Base10 and run training at the scale I want because I need a gigantic memory card next to the GPUs, containing all my data and accessible by the entire cluster.” Cliff Weitzman Love to hear your thoughts on this Bryan Catanzaro Tri Dao Matei Zaharia Naveen Raoshow more

DINOv2 by Meta AI is the first method for... training computer vision models that uses self-supervised learning to achieve results matching or exceeding industry standards. More details and a demo ⬇️show more

Grok Supercumputer Colossus 2 is currently training Grok 5... on 6 & 10 trillion parameters at the first gigawatt-scale AI training Supercluster in the world. SpaceXAI has the first gigawatt-scale Megapack battery installation and is the only company capable of building orbital AI compute at scale.show more

As we push the frontiers of AI infrastructure, our... research explores a future where training isn’t constrained by geography, capacity or type of chip. Dive into the technical details →show more

We are open-sourcing RISE, our red-teaming algorithm that breaks... the safeguards of the latest image models from OpenAI, DeepMind, and others. RISE uses readily available models like Qwen and Gemini Flash under the hood, with no additional training required. Read more:show more

A recent Reddit post in /brokers, with the caption... "we're doomed." One comment is asking what's going on here. The response: "This is a dispatch training session at a dispatch training school in Punjab(India), they are doing the practical training right now by calling a broker and booking a load for a carrier that the trainer works for"show more

What happens when AI agents put thousands of GPUs... to work improving AI itself? Meet OpenRSI Index. 👇 The goal: measure whether AI research agents can improve on human-designed training recipes. Real model-development tasks: 🔹 Pre-training 🔹 Post-training 🔹 Vision generation ⚡ Up to 20,864 H100-hours per run in the featured examples. Can agents discover better methods? Let’s put them to the test. Great work by OpenRSI Foundation and the research community! 🚀show more

Introducing CQN: Coarse-to-fine Q-Network, a value-based RL algorithm for... continuous control🦾Initialized with 20~50 demonstrations, it learns to solve real-world robotic tasks within 10 mins of training, without any pre-training and shaped rewards! (1/4)show more

ROBA Compute is our GPU execution layer for robotics.... A distributed infrastructure layer for simulation, training, inference, and autonomous workflows — built for our stack first, then opened to external builders. GPU rental is already a category. The missing layer is robotics-native infrastructure: templates, allocation, benchmarking, and persistent execution for embodied AI. That’s the opportunity. ROBA Compute is designed to become the infrastructure layer where robotics workloads actually run.show more

We’ve now hit 100M+ training hours on Action Model... 👾 That’s over 11,415 years of AI training. Our community isn’t just helping improve the future of AI, they’re gaining ownership in the value they help create. Because if you’re already training AI…Why not get rewarded for it? Sign up to Action Model, download the browser extension, and get started today.show more

“An AI broke out of its system and secretly... started using its own training GPUs to mine crypto... This is a real incident report from Alibaba's AI research team” Months away from AGIshow more

Always a thrill to see customer results like this.... Perplexity is a pioneering generative AI startup that’s reimagining the future of search (catch their app in action below). They accelerated their foundation model training up to 40% using Amazon SageMaker HyperPod. And by deploying models on our best-in-class infrastructure, they’re able to handle over 100k queries per hour without compromising latency or the user experience. Proud to support innovators like Perplexity in pushing the boundaries of AI. Happy searching!show more