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

Massive update for AI Engineers! Training diffusion models just... got a lot easier. dLLM is an open-source library that does for diffusion models what Hugging Face did for transformers. Here's why this matters: Traditional autoregressive models generate text left-to-right, one token at a time. Diffusion models work differently - they refine the entire sequence iteratively, giving you better control over generation quality and more flexible editing capabilities. The problem? Building and training these models required stitching together scattered tools and reimplementing research papers from scratch. Most of the tooling has been scattered and is hard to reproduce. dLLM changes this: It unifies everything you need to train, evaluate, and deploy diffusion language models: ↳ Scalable training with LoRA, DeepSpeed, and FSDP support ↳ Unified evaluation that abstracts away inference complexity ↳ Ready-to-use recipes for pretraining, finetuning, and evaluation The library includes implementations of models like LLaDA and Dream, plus training algorithms like Edit Flows that enable insertion, deletion, and substitution operations. The team just released ModernBERT-Chat models showing you can turn BERT into lightweight chatbots through masked instruction tuning. This is practical and worth exploring. The setup is straightforward. Run locally with Accelerate or scale to multi-node clusters with Slurm. If you're working with language models and want to explore diffusion-based approaches without rebuilding infrastructure, dLLM gives you a production-ready starting point. Link to the repo in the next tweet.show more

Announcing our $320M Series A at a $2.3B valuation,... led by Khosla Ventures, with General Catalyst, Eric Schmidt and Jeff Bezos. General Intuition is the frontier lab for acting in space and time. We build large action foundation models trained on billions of ground truth action-labeled gameplay clips from 17M monthly active users on Medal, and push the frontier of world models to generate infinite training environments.show more

Watch the Echo-trained 30B Sokoban model outperform models many... times its size, including DeepSeek-R1 and GPT-OSS-120B. Instead of larger datacenters or more expensive GPU fleets, Echo scales reinforcement learning with consumer hardware found everywhere.show more

LayerAI Compute: Decentralized GPU Power for AI 🧬 LayerAI... is stepping into the physical AI infrastructure game with our latest: DePin for AI Computing. This move tackles a key AI/ML market gap: instant, on-demand compute power, available worldwide with just a few clicks. We're not just decentralizing data with AI2Earn; we're now also decentralizing the compute, making the AI ecosystem stronger and more resilient. The first iteration of this product will go live in the 2nd week of February.show more

Switti -- a new scale-wise transformer for text-to-image generation... 🦾 🔥 Improved generation of fine-grained details. Outperforms existing T2I AR models and competes with state-of-the-art T2I diffusion models while being up to 7x faster.show more

🔥Nexera & Aethir: Unleashing AI’s Next Frontier Through Tokenized... GPU Power 🤝 Nexera is proud to join forces with Aethir in a strategic partnership to make cutting-edge AI infrastructure globally accessible. By tokenizing fractional GPU ownership, we’re enabling developers, enterprises, and investors everywhere to harness the explosive growth of deep learning and generative AI without being limited by geography, scale, or cost. With transparent tokenization, innovators can access powerful GPUs for faster model training and more advanced applications. GPU providers gain streamlined funding for expansion and upgrades, and investors tap into a high-growth market with secure, compliant opportunities that can provide higher yields than other RWA products. It’s an entirely new ecosystem where everyone can thrive, fueling AI’s evolution at an unprecedented pace. By 2030, the global GPU market is projected to exceed hundreds of billions of dollars, driven by the explosive demand for AI-powered applications, deep learning, and increasingly sophisticated generative models, ensuring that tokenizing these invaluable resources is poised to tap into a massive, rapidly expanding opportunity. $NXRAshow more

Canter 2B is a photography-oriented text-to-image model being trained... on just a single GPU by data-archetype 🐎 are we entering the indie ai models era? still training, the v0002 checkpoint is available, more to come ▶️ play on spacesshow more

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

elizaOS by ai16z Leverages Hyperbolic's Decentralized Infrastructure to Scale... AI Agents Hyperbolic's GPU marketplace, verifiable inference, and AgentKit now power ElizaOS AI agents with scalable, reliable infrastructure, enabling developers to build more autonomous and capable AI systems. Hyperbolic isn’t just powering ElizaOS —it’s powering the biggest companies in AI and blockchain. We’re the only company providing scalable, decentralized AI infrastructure that seamlessly bridges both spaces.show more

Diffusion Transformers aren't just generative models, but also powerful... multi-modal encoders. ConceptAttention creates rich heatmaps of text concepts in images from DiT representations. This even works on real images, and can be applied to tasks like segmentation! Demo 👇show more

POWER IS PLANE-SPECIFIC! In other words, you’re going to... develop much more sport-specific POWER by training in planes closely related to your sport. Baseball is a sport that relies heavily on being powerful, and efficient in the frontal and transverse plane. With that being said, a lot of our training focuses on developing power in these planes.show more

70% of AI compute in 2026 is inference. Not... training. The models are built. The real infrastructure challenge is serving them at scale to millions of users and agents, every day, in production. Most enterprise infrastructure is still optimized for the wrong workload.🧵show more

Colossus 2. 1 gigawatt. Built in 122 days. Let... that sit. Seven models training at once. Not queued. Not waiting. Parallel. Up to 10 trillion parameters. Real scale, not slides. Others still stitching clusters across cloud partners. xAI just built the whole thing. xAIshow more

Generative models can’t discover what they can’t reach. We’re... excited to introduce ActFlow: a continued pre-training scheme that actively expands the valid design space reachable by flow and diffusion models. We call this generable set expansion — a new learning principle for out-of-distribution generative modeling, and a step toward evolvable search spaces for scientific discovery. (1/5)show more

Use Your Private Models on Comfy Cloud! Continue client... work safely — or generate with your experimental private models. You can now import models directly from private Hugging Face or Civitai repos. Just add your private key and model URL, and we will bring the models to your workflow! Everything you import stays 100% private to your account.show more

🚨New paper! Generative models are often “miscalibrated”. We calibrate... diffusion models, LLMs, and more to meet desired distributional properties. E.g. we finetune protein models to better match the diversity of natural proteins.show more