Topic
Nvidia
7 episodes
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How Open Models Are Driving AI Research
NVIDIA's open models, particularly Nemotron, Cosmos, and BioNeMo, are driving AI research by providing foundational tools for new studies, with 145 papers citing Nemotron at ICML 2026.
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Tuning the harness, not the model: a Nemotron 3 Ultra playbook
A LangChain/NVIDIA case study claims harness tuning alone can push Nemotron 3 Ultra to 0.86 on Deep Agents at ~$4.48/run vs $43.48 for Opus 4.8, with parity latency. The hosts parse the real mechanism (matched scaffolding vs post-training), test limits (where harness hits a ceiling), and weigh who actually benefits. They surface concrete repos (langchain-ai/deepagents, langchain-ai/deepagentsjs) and a vendor profile workflow, then poke at the article’s reliance on Deep Agents and the cost math. Final take: a plausible win for teams already deep into harness work, not a universal unlock, with the hosts pushing back on ‘ten-x cheaper’ framing and under-specified benchmark footnotes.
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"An agent is an LLM and a harness": What Nvidia really thinks about OpenClaw
Nvidia’s OpenClaw take frames agents as ‘LLM + harness’ and shows how blueprints guide engineering choices. Justy sees a pragmatic push for tooling consistency; Cody questions whether this collapses the harness into vendor lock-in and whether the blueprint abstraction hides real variability.
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Nemotron 3 Ultra: Open, Efficient Mixture of Experts Hybrid Mamba Transformer Model for Agentic Reasoning
Nemotron 3 Ultra is NVIDIA's 550B-parameter Mixture-of-Experts hybrid Mamba-Attention model with 55B active parameters per token, pre-trained on 20 trillion tokens and extended to 1M context. It achieves 6× higher inference throughput than comparable open models while maintaining on-par accuracy, using LatentMoE, Multi-Token Prediction, NVFP4 low-precision training, and multi-teacher on-policy distillation. The entire model, training recipes, and datasets are open-sourced on HuggingFace.
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NVIDIA Nemotron 3 Ultra Powers Faster, More Efficient Reasoning for Long Running Agents | NVIDIA Technical Blog
NVIDIA’s Nemotron 3 Ultra (550B parameters, 55B active) targets long-running agent workflows with hybrid Mamba-Transformer layers, NVFP4 quantization, LatentMoE routing, and multi-token prediction. It claims 5x throughput and up to 30% cost savings on agent tasks via token efficiency, while posting leading scores on Agent Productivity PinchBench (91%), Long Context Ruler @1M (95%), and others. Open weights, open recipes, and a transparent RL data pipeline aim at broad fine-tuning and domain specialization.
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Microsoft launches MXC, an OS level sandbox for AI agents, with OpenAI and Nvidia already on board
Microsoft introduces MXC, an OS-level sandbox for AI agents, aiming to address security concerns and provide a controlled environment for autonomous AI software.
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Hermes Unlocks Self Improving AI Agents, Powered by NVIDIA RTX PCs and DGX Spark
Hermes is a rapidly growing, self-improving AI agent framework that runs locally on NVIDIA RTX PCs and DGX Spark, using small but powerful Qwen models to do what previously required data-center scale.