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NVIDIA PAIR Virtual Inference Router Expands Available Compute on Your Local Network

AI agents are learning to do more by working together. A lead agent can break a complex task into smaller jobs and assign those jobs to specialized subagents….

AI agents are learning to do more by working together. A lead agent can break a complex task into smaller jobs and assign those jobs to specialized subagents. Additionally, users are starting to run multiple agent sessions at the same time. Multi-agent workflows for accomplishing complex tasks are also becoming more common. This breadth-first approach can improve the speed of task completion…

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‘NBA 2K27’ With NVIDIA DLSS 5 Leads 26 New Games Coming to GeForce NOW

September is here with 26 more games streaming on GeForce NOW this month, led by a slam dunk: NBA 2K27 with the NVIDIA DLSS 5 3D-Guided Neural Rendering feature. Through NVIDIA’s close collaboration with Visual Concepts and 2K, DLSS 5 brings a new level of lifelike lighting and material detail to the court — tuned […]

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NeoMME: an efficient Multimodal-native and Multilingual Encoder

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NVIDIA to Acquire Hugging Face

I’m excited to announce that NVIDIA has agreed to acquire Hugging Face for $12,930,300,000. Together, we will scale Hugging Face’s platform, strengthen its infrastructure and expand access to AI for developers and institutions worldwide. Over the past decade, Clem, Julien, Thomas and the team at Hugging Face have built something remarkable: a vibrant home for […]

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Fine-tuning a 350M Model for Better Structured Outputs in 100 GRPO Steps

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Give Your Coding Agents a Memory You Own

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Training a coding model to paint watercolours with TRL and OpenEnv

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The Modern CUDA Toolbox in Practice: A Step-by-Step Optimization Walkthrough

NVIDIA CUDA remains the foundation of GPU-accelerated computing, powering everything from scientific simulations to large-scale AI training. But writing…

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Co-Designing AI Models Using Speculative Decoding for Faster LLM Inference

This post is the third in a series on AI model co-design. It explores how to accelerate LLM inference while maintaining accuracy using speculative decoding and…

This post is the third in a series on AI model co-design. It explores how to accelerate LLM inference while maintaining accuracy using speculative decoding and offers five guidelines for selecting draft length and draft mechanism across the Pareto frontier. For a discussion of how model design choices impact both throughput and interactivity without sacrificing accuracy, see AI Model Co…

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Real-Time Intelligence with IBM Time Series Models on Confluent