{"slug": "distributed-ai-on-aws", "title": "Distributed AI on AWS", "summary": "AWS released a comprehensive guide for distributed AI training on its infrastructure, featuring reference architectures, test cases, and best practices for frameworks like PyTorch, Megatron-LM, NeMo, and JAX. The guide includes Dockerfiles, Slurm scripts, and Kubernetes manifests to help users train large-scale models on AWS compute platforms such as HyperPod, ParallelCluster, and EKS.", "body_md": "#\nAWSome\n\nDistributed AI\n\nReference architectures, test cases, and best practices for training large-scale models with PyTorch, Megatron-LM, NeMo, JAX, and more on AWS infrastructure.\n\n## Training Frameworks\n\nProduction-ready examples grouped by framework. Each includes Dockerfiles, Slurm scripts, and Kubernetes manifests.\n\n[ 🔥 FSDPDDPDeepSpeedTorchTitanPicotronvLLMTRLOpenRLHF ](/frameworks/pytorch)\n\n### PyTorch\n\nNative distributed training with DDP, FSDP, TorchTitan, DeepSpeed, and more. Covers LLMs, vision, robotics, and RLHF.\n\n[ ⚡ Megatron-LMNeMoNeMo RLBioNeMo ](/frameworks/megatron)\n\n### Megatron\n\nNVIDIA Megatron-LM and NeMo for large-scale LLM pre-training with tensor, pipeline, and expert parallelism.\n\n[ 🧬 PaxMLXLATPU/GPU ](/frameworks/jax)\n\n### JAX\n\nGoogle JAX with PaxML for distributed training leveraging XLA compilation and automatic parallelism.\n\n[ 🧠 NeuronXOptimum NeuronTrainium ](/frameworks/neuron)\n\n### AWS Neuron / Trainium\n\nNeuronX Distributed for training on AWS Trainium & Inferentia chips with optimized compilers.\n\n[ 🤖 Isaac LabOpenVLAV-JEPA 2nanoVLM ](/frameworks/physical-ai)\n\n### Physical AI & Robotics\n\nEmbodied AI training with NVIDIA Isaac Lab, OpenVLA, V-JEPA2, and vision-language-action models.\n\n[ 🎯 TRLvERLSLIMEPPODPO ](/frameworks/reinforcement-learning)\n\n### Reinforcement Learning\n\nRLHF, DPO, PPO, and scalable RL frameworks for LLM alignment and post-training.\n\n[ 🧪 DistillationCompressionTransfer Learning ](/frameworks/model-customisation)\n\n### Model Customisation\n\nKnowledge distillation, compression, and model adaptation techniques for production.\n\n## Reference Architectures\n\nCloudFormation templates and deployment guides for every AWS compute platform.\n\n## Get Started in Minutes\n\nThree steps to launch your first distributed training job.\n\n### Deploy Infrastructure\n\nLaunch a cluster using our CloudFormation templates for HyperPod, ParallelCluster, or EKS.\n\n### Build Container\n\nUse our Dockerfiles to build a training container with your framework of choice.\n\n### Launch Training\n\nSubmit your job with Slurm or Kubernetes using our ready-made launch scripts.", "url": "https://wpnews.pro/news/distributed-ai-on-aws", "canonical_source": "https://www.day1training.com/", "published_at": "2026-06-17 09:11:09+00:00", "updated_at": "2026-06-17 09:23:58.982402+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "large-language-models", "ai-infrastructure", "ai-tools"], "entities": ["AWS", "PyTorch", "Megatron-LM", "NeMo", "JAX", "NVIDIA", "Google", "Trainium"], "alternates": {"html": "https://wpnews.pro/news/distributed-ai-on-aws", "markdown": "https://wpnews.pro/news/distributed-ai-on-aws.md", "text": "https://wpnews.pro/news/distributed-ai-on-aws.txt", "jsonld": "https://wpnews.pro/news/distributed-ai-on-aws.jsonld"}}