We need a lot more power and compute
COMPUTING
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PyTorch Compilation Strategy for Large-Scale Transformer Models
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1. there is no fork of pytorch fwiw. only a fork of nccl. clearly you overheard wrong.
2. torch.compile wasn't even considered because it doesnt have precompilation yet and that was deemed a requirement for large-scale xlformers runs. so the whole thing about graph breaks is -
Apache Spark 4.0 transformWithState API for Operational Monitoring
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Learn to build operational monitoring pipelines using Apache Spark™ 4.0's new transformWithState API This blog walks through how to use the new transformWithState API to:
-Track, alert on, and analyze sensor data with ValueState, ListState, and MapState
-Set up time-based state -
Axolotl: YAML-based LLM fine-tuning and LoRA optimization framework
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4. Axolotl • Yaml-based setup for fine-tuning, LoRA/QLoRA, DPO, GRPO, and multimodal workflows
• Includes kernel optimizations for memory-efficient training GitHub repo: -
DeepSpeed: Distributed Fine-Tuning Framework for Large Language Models
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3. DeepSpeed • Built for large-scale distributed fine-tuning with ZeRO and FSDP
• Optimized for multi-GPU and multi-node training with advanced memory management
• Trusted in production environments for scalable LLM training GitHub repo: -
Jupyter Notebooks in Production: The AI Prehistory New Experts Ignore
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95% of the new "AI experts" on LinkedIn never experienced the good old days when some people wanted to put Jupyter Notebooks into production. That was the real prehistory of AI: explaining to managers that, well, no, we don't do that… Just because MNIST runs.
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Building AI at Scale: Energy and Resources Infrastructure
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Brasilia for less than 24 hours… what a fascinating city. My message was that creating ‘intelligence’ as a commodity is a major industrial undertaking. When you look at the whole ‘stack’ that is required to make AI a cheap and abundant resource at scale – energy and rare earth
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Groq and ToothFairyAI Melbourne Session: Fast AI at Scale
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Melbourne devs, come talk AI with us. Groq and ToothFairyAI are hosting a session on Building Fast and at Scale Reliably. Details in comments.
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AI Stack Fundamentals Need Major Improvements Across All Layers
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Every part of the AI stack – semiconductors, GPUs, Python, PyTorch, LLMs, post-training, etc. – is in major need of improvement.
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Graphics 3.0: AI Superpowers Ray Tracing and Rasterization
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"We’re now in graphics 3.0—ray tracing, rasterization, and now, everything superpowered by AI." — Ming-Yu Liu, VP of Research, Deep Imagination Lab at NVIDIA.
— NVIDIA AI (@NVIDIAAI) 15 août 2025
Hear Ming-Yu, along with our research leaders Sanja Fidler and Aaron Lefohn, explore how breakthroughs in computer… pic.twitter.com/x2O4g8Vp60"We’re now in graphics 3.0—ray tracing, rasterization, and now, everything superpowered by AI." — Ming-Yu Liu, VP of Research, Deep Imagination Lab at NVIDIA. Hear Ming-Yu, along with our research leaders Sanja Fidler and Aaron Lefohn, explore how breakthroughs in computer