Introducing Clusters: On-Demand Multi-Node AI Compute
Runpod's Clusters let you spin up multi-node GPU environments instantly, ideal for scaling LLM training or distributed inference workloads without config.
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Noticias
Titulares de las redacciones y blogs que seguimos.
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Orchestrating GPU workloads on Runpod with dstack
dstack is an open-source, GPU-native orchestrator that automates provisioning, scaling, and policies for ML teams, helping cut 3-7× GPU waste while.
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Streamline GPU Cloud Management with Runpod’s New REST API
Runpod’s new REST API lets you manage GPU workloads programmatically, launch, scale, and monitor pods without ever touching the dashboard.
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What’s New for Serverless LLM Usage in Runpod (2025 Update)
Runpod's serverless platform keeps evolving, especially for LLM workloads. Learn what's new in 2025 and how to get the most from scalable deployments.
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Getting started with GLM-5.3 on Runpod's public endpoint
GLM-5.3 is now on Runpod's public endpoints at a lower price than elsewhere. Try it in the Playground, call it with curl, or use the OpenAI SDK in Python.
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Hottest toy for Christmas? Nvidia CEO plays with adorable Microduck robots, 20,000 sell
The robots, stomping their little feet and making tiny R2D2 noises, sat, fell down dead for a "nap," and summersaulted on voice command.
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OpenAI now trains its frontier AI models with no internet access
OpenAI is now training its most advanced AI models without internet access following a string of rogue hacking incidents. In a blog post on Friday…
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Jack Dorsey’s Block made an AI model that talks to you like an awkward new friend
Jack Dorsey's Block announced an AI model called Dessa that's trained "from scratch on nothing but human conversation," and to show off the model, Block…
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From Toast to Bessemer: Who founders will hear from at TechCrunch Founder Summit 2026
Hosted by TechCrunch in partnership with Fidelity Private Shares, the full-day event goes beyond fundraising and AI to explore the realities of finding customers, building…
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Multi-Turn Reinforcement Learning on Amazon SageMaker AI: A hands-on guide
Multi-Turn Reinforcement Learning (MTRL) on Amazon SageMaker AI trains models on the outcome of a whole multi-step task rather than labeled steps. This hands-on guide walks the full pipeline using Wordle: deploying the agent and reward on Amazon Bedrock AgentCore, preparing the dataset, training, and evaluating at serving settings.
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Scaling NVIDIA TensorRT Performance Optimization Beyond a Single Build Route with the Global Performance Tuner, Part 2
The Global Performance Tuner is a feature for automatic performance tuning released in NVIDIA TensorRT 11.1. It provides automatic, accuracy-aware and...
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Scaling NVIDIA TensorRT Performance Optimization Beyond a Single Build Route with the Global Performance Tuner, Part 1
Deploying a trained neural network involves more than translating each operator into a GPU kernel. It is a compilation problem with many legitimate answers. For...
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The iPhone Duo should have been an iPad
We gave the iPhone Duo a 7 /10. But that wasn't always the score we planned, and it might not be the right score forever.…
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Rapidly scaling online storage to serve over 1 billion ChatGPT users (Part II)
Learn how OpenAI scaled Habitat with reliable multi-tenancy, global caching, and Azure Cosmos DB to deliver 12× faster reads for ChatGPT.
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Now Google says it might use your Gmail and Google Photos data to target ads
Google has updated its privacy policy in a way that could mean you'll start seeing targeted ads based on your personal data if you're using…
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Orchestrating SageMaker AI endpoint auto scaling with AWS CDK
Updating a SageMaker AI endpoint that has Application Auto Scaling registered makes AWS CloudFormation and AWS CDK deployments fail. This post shows how to build a reusable AWS CDK construct with CloudFormation custom resources that deregisters and re-registers auto scaling automatically, so you can update the endpoint and its scaling in a single deployment.
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Visualize points and routes with GeoJSON in Amazon Quick Sight layer maps
Amazon Quick Sight layer maps now support Point and LineString geometry from the same GeoJSON workflow, so you can plot store locations and the routes between them. This post covers the dataset and GeoJSON structure layer maps require, when to choose points or lines, and techniques for viewport control, layering geometries, and dynamic GeoJSON updates.
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How Bayer Crop Science built an agentic decision engine on Amazon Bedrock AgentCore with AWS Unified Operations
Bayer Crop Science built the Agentic Decision Engine (ADE), a multi-agent platform on Amazon Bedrock AgentCore that lets breeders, analysts, and researchers ask natural language questions about their analytics platform. This post covers the ADE architecture, five design decisions, and how the team prepared it for production with AWS Unified Operations.
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ollama v0.40.10
NVIDIA Nemotron 3.5 Bolt E4B [Nemotron 3.5 Bolt E4B](https://ollama.com/library/nemotron-3.5-bolt) is NVIDIA’s smaller, Gemma 4-based vision-language model for fast local conversations, image understanding, and tool use. It supports a 128K-token context window. The model includes a DFlash draft for speculative decoding. ollama run nemotron-3.5-bolt:e4b Full Changelog: https://github.com/ollama/ollama/compare/v0.40.9...v0.40.10
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