Distributed training on M4 Mac Mini cluster We implemented... Google DeepMind DiLoCo on Apple Silicon to train large models with 100-1000x less bandwidth compared to DDP baseline. AI is entering a new era where a distributed network of consumer devices can train large models.show more

Running DeepSeek-V3 on M4 Mac Mini AI Cluster 671B... MoE model distributed across 8 M4 Pro 64GB Mac Minis. Apple Silicon with unified memory is a great fit for MoE.show more

M4 Mac Mini AI Cluster Uses EXO Labs with... Thunderbolt 5 interconnect (80Gbps) to run LLMs distributed across 4 M4 Pro Mac Minis. The cluster is small (iPhone for reference). It’s running Nemotron 70B at 8 tok/sec and scales to Llama 405B (benchmarks soon).show more

if you’re confused about how large language models work... this new animation from google deepmind should clear a lot of things upshow more

Preparing to run the new Llama 3.3 70B on... my mac cluster. Downloading model shards onto 3 x M4 Pro Mac Mini and 1 x M3 Max MacBook Pro. AI cluster is connected by Gigabit ethernet switch with EXO Labsshow more

Day 1: Benchmarks We ran 1000+ LLM benchmarks on... real consumer devices. Data includes single-device and multi-device clusters with Tokens-Per-Second (TPS) and Time-To-First-Token (TTFT). Setups tested: 3x M4 Mac Mini cluster, iPhone 15 + S24, RTX4090 & more.show more

Presenting the world’s first autonomous, on-demand data network for... specialized AI models. Turf DevNet is live on the Avalanche🔺 network🔺 Builders can now use contextual Data Feeds to train smarter AI 🧵show more

A teaser of DeepCoreCloud! With 100+ GPUs in a... high-performance cluster, we’re bringing 24/7 AI computing to the world. Train models, deploy AI, and scale without limits! DeepCoreCloud is built for innovators. The future of AI infrastructure is almost here. Are you ready? #DeepCoreCloud #ItronixAI #GPUClustershow more

m4 mac mini cluster with 5 nodes running exo

Artificial Intelligence (#AI) models that process and interpret visual... data are called large vision models. These models, like large language models, are trained on large amounts of data. #midjourney #dalle #stablediffusion (1/8) 🧵 ⤵️show 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

🌐 Experience the Power of Edge Computing with Hyra... AI! We’re scaling at microsecond speed with Train-to-Earn: ⚡ 7.5B+ AI jobs completed 📱 2.2M+ active devices 💰 528M+ $HYRA rewards distributed Join a global movement of AI builders & earners. 🍎 iOS: 📱 Android:show more

🌍 The future is decentralized. PhyChain is building the... global computing power network — connecting idle devices, empowering AI, IoT, and Web3. Join the new era of distributed computing! 🚀 #DePIN #Web3 #AI #IoT #Blockchain #PhyChainshow more

As a supporter of the open weights ecosystem, we're... proud to be a post-training partner for NVIDIA Nemotron. We post-train Nemotron models for customer use cases, de-risk mainline RL runs on our AC2 platform and training stack, and contribute aggregate workload statistics for inference benchmarking. This is how open models get better, and we're excited to keep working closely with NVIDIA AI.show more

We've just surpassed 3 million models on the Hub... 🤗 the community is accelerating towards an open, distributed future where open AI is everywhere, for everyone 🚀show more

you can just ▲ ai gateway models ⬩ 1... api key to rule them all ⬩ AI SDK & OpenAI compat ⬩ globally redundant and distributedshow more

The new M4 Mac Mini is sooo mini!!

Training large models with RL typically runs on a... fixed homogeneous cluster of the latest accelerators for numerical stability and faster throughput. This throws away inference optimization work done for other vendors and generations, limits the capacity you can utilize, and is unnecessarily expensive. So we trained a model on agentic coding tasks using fully asynchronous RL and mixed-precision, mixed-accelerator samplers. With off-policy correction, training stayed stable through convergence, on par with homogeneous samplers, at a lower cost.show more

LLMs + Elixir: distributed tracing for targeted processes anywhere... on the cluster with realtime UI. Idea to shipped, sub 30min:show 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