Tag: pytorch

  • Docker: Behind the scenes: How we designed Docker Model Runner and what’s next

    Source URL: https://www.docker.com/blog/behind-the-scenes-how-we-designed-docker-model-runner-and-whats-next/ Source: Docker Title: Behind the scenes: How we designed Docker Model Runner and what’s next Feedly Summary: The last few years have made it clear that AI models will continue to be a fundamental component of many applications. The catch is that they’re also a fundamentally different type of component, with complex…

  • Cloud Blog: Train AI for less: Improve ML Goodput with elastic training and optimized checkpointing

    Source URL: https://cloud.google.com/blog/products/ai-machine-learning/elastic-training-and-optimized-checkpointing-improve-ml-goodput/ Source: Cloud Blog Title: Train AI for less: Improve ML Goodput with elastic training and optimized checkpointing Feedly Summary: Want to save some money on large AI training? For a typical PyTorch LLM training workload that spans thousands of accelerators for several weeks, a 1% improvement in ML Goodput can translate to…

  • Cloud Blog: Introducing the next generation of AI inference, powered by llm-d

    Source URL: https://cloud.google.com/blog/products/ai-machine-learning/enhancing-vllm-for-distributed-inference-with-llm-d/ Source: Cloud Blog Title: Introducing the next generation of AI inference, powered by llm-d Feedly Summary: As the world transitions from prototyping AI solutions to deploying AI at scale, efficient AI inference is becoming the gating factor. Two years ago, the challenge was the ever-growing size of AI models. Cloud infrastructure providers…

  • Cloud Blog: AI Hypercomputer developer experience enhancements from Q1 25: build faster, scale bigger

    Source URL: https://cloud.google.com/blog/products/compute/ai-hypercomputer-enhancements-for-the-developer/ Source: Cloud Blog Title: AI Hypercomputer developer experience enhancements from Q1 25: build faster, scale bigger Feedly Summary: Building cutting-edge AI models is exciting, whether you’re iterating in your notebook or orchestrating large clusters. However, scaling up training can present significant challenges, including navigating complex infrastructure, configuring software and dependencies across numerous…

  • Slashdot: Microsoft Brings Native PyTorch Arm Support To Windows Devices

    Source URL: https://tech.slashdot.org/story/25/04/24/2050230/microsoft-brings-native-pytorch-arm-support-to-windows-devices?utm_source=rss1.0mainlinkanon&utm_medium=feed Source: Slashdot Title: Microsoft Brings Native PyTorch Arm Support To Windows Devices Feedly Summary: AI Summary and Description: Yes Summary: Microsoft’s release of PyTorch 2.7 with native support for Windows on Arm devices marks a significant development for machine learning practitioners, particularly those focusing on AI tasks. This update enhances the ease…

  • Simon Willison’s Weblog: Gemma 3 QAT Models

    Source URL: https://simonwillison.net/2025/Apr/19/gemma-3-qat-models/ Source: Simon Willison’s Weblog Title: Gemma 3 QAT Models Feedly Summary: Gemma 3 QAT Models Interesting release from Google, as a follow-up to Gemma 3 from last month: To make Gemma 3 even more accessible, we are announcing new versions optimized with Quantization-Aware Training (QAT) that dramatically reduces memory requirements while maintaining…