Topic
Nvidia
11 episodes
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NVIDIA Nemotron 3.5 Super VL Powers Specialized Agents with Multimodal Reasoning | NVIDIA Technical Blog
NVIDIA drops Nemotron 3.5 Super VL — a 120B-parameter open-weight multimodal MoE model built for specialized enterprise agents. Jessica and Cathy dig into the real argument: post-training on your own data beats the frontier on your specific task, and the efficiency story (40x inference cost reduction, 10x latency) is where the product actually lives.
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NVIDIA's Nemotron 3.5 Super VL with post training beats Claude Opus 5, GPT 6.1 Sol on specialized task benchmarks
Edmund and Geffen dig into NVIDIA’s Nemotron 3.5 Super VL launch, with Geffen skeptical of benchmark-cherry-picked claims and Edmund focused on the product reality: a multimodal model that can be post-trained on enterprise docs, tools, and policies, plus local Bolt variants for smaller deployments. They land on the idea that the real story is not “it beats Claude or GPT” in the abstract, but that NVIDIA is selling an adaptable stack with deployment control, while the public benchmark chart still needs independent verification.
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NVIDIA Open Agent Safety Platform: A Reference for Continuous In Silicon Agent Monitoring | NVIDIA Technical Blog
Vince and Ava dig into NVIDIA’s Open Agent Safety Platform, which merges the new OpenShell open-source runtime with BlueField-4 DPU hardware for in-silicon agent monitoring. Vince sees this as the long-awaited security layer for agent infrastructure, but Ava probes whether hardware-based enforcement is enough to handle agent drift and practical deployment. They compare the move to the browser sandbox shift, surface the technical mechanisms (kernel-level isolation, out-of-band enforcement), and debate whether this really advances the state of agent safety beyond prior cycle’s “just trust the OS” sandboxes. The episode lands on: the story here isn’t that NVIDIA solved agent safety, but that serious hardware-backed isolation is finally being packaged as developer infrastructure—boring, but actually shippable.
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NVIDIA Brings Simplified Local AI Support To NVIDIA GPUs Carrying 24+ GB VRAM While vLLM & & llama.cpp Optimizations Boost Compute By Up To 1
NVIDIA ships kernel optimizations for local AI inference on RTX and DGX platforms, delivering up to 1.9x performance gains through vLLM and llama.cpp, paired with one-click agent setup (Perplexity Portable Computer, Hermes Agent, OpenClaw) for GPUs with 24+ GB VRAM. The move collapses the friction between local-first and cloud-capable workflows.
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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.