016 - Nemotron 3 Ultra: NVIDIA’s Open-Weights Frontier Agent Brain (1M Context, 5x Faster)

016 - Nemotron 3 Ultra: NVIDIA’s Open-Weights Frontier Agent Brain (1M Context, 5x Faster)

Johan breaks down NVIDIA’s ComputeEx 2026 announcement of Nemotron 3 Ultra 550B-A 55B, an open-weights mixture-of-experts model with 550B total parameters and 55B active, positioned as an orchestration “agent brain” for multi-step tasks behind the firewall. He reviews NVIDIA’s benchmarks versus GLM 5.1, Kimi K 2.6, and Qwen 3.5, highlighting best-in-class instruction following (82%), long-context performance (95%) with a 1M-token window, strong agent productivity (91%), and weaker coding results on TerminalBench versus Kimi. Johan emphasizes reported advantages in speed (~300 tokens/sec, ~5x faster), cost (up to ~30% cheaper on SWE-bench tests), and deployability via a unified NVFP4 checkpoint optimized for H100 and B200 GPUs, plus NemoClaw as the agent blueprint. He closes with an early-access demo comparing two agents researching Netherlands’ 2026 World Cup odds, showing Nemotron’s more granular path analysis and a 5.8% win estimate.00:00 Private AI Lab Intro01:19 Nemotron Ultra Explained02:22 Agent Brain Focus03:07 Benchmark Reality Check05:14 Speed And Cost Edge06:11 Training And Precision08:02 NeMo Claw Agents08:58 World Cup Agent Demo12:22 Why This Matters13:17 Wrap Up And Links

015 - Meet Sparky: A Real-Life Jarvis with Alexis Gallagher

I've been trying to build my own Jarvis for years. Then I met Alexis Gallagher at GTC — and Sparky is the closest thing I've seen. Alexis is an AI researcher and developer, formerly at Answer AI and Google, now building something most people in AI aren't: a robot designed not just to be useful, but to be *alive*. Sparky lives on his desk in San Francisco. He initiates conversations. He develops his own evolving interests — eels, catenary arches, abandoned infrastructure. He knows who's in the room, when to speak, and when to stay quiet. And he noticed when it was Alexis's first Friday after leaving his job. In this episode we go deep on the two design goals behind Sparky (useful and alive), the OpenClaw orchestration layer, the social awareness architecture running five times per second, the shared workspace principle that unlocks genuinely useful AI at a desk, and the tradeoffs between cascading and voice-to-voice architectures. We also do a live model switch mid-episode — from Claude Sonnet 4.6 to Nemotron 3 Super 120B running locally on a DGX Spark. It goes impressively well. Until it doesn't. That's in there too. Guest Alexis Gallagher — AI researcher and creator of Sparky 🌐 myrobotSparky.com 🔗 https://www.linkedin.com/in/alexis-gallagher/ Key topics covered - The two design goals: useful AND alive — and why "alive" is the one almost nobody builds for - How Sparky develops and evolves - The social awareness stack - What OpenClaw enables - The shared workspace principle - Cascading architecture (STT → LLM → TTS) vs voice-to-voice — the intelligence tradeoff - Hardware: Reachy Mini Lite, RTX 3090, DGX Spark, Raspberry Pi — the full spectrum - Live model switch: Claude Sonnet 4.6 → Nemotron 3 Super 120B (the Flowers for Algernon moment) - The future of personal AI — why embodied social presence is the natural human interface Chapters ``` 00:00 Introduction 00:39 Who is Alexis Gallagher? 01:04 The pivotal AI moment: speech recognition in 2015 03:14 Science fiction to reality — where are the talking robots? 04:22 Sparky introduces himself (live on air) 05:33 The two design goals: useful and alive 07:02 How Sparky initiates conversations — and why that changes everything 08:10 Organic interests: how Sparky evolves what he cares about 09:48 OpenClaw as orchestration layer — soul.md and body control 12:55 Defining a custom robot node type in OpenClaw 15:26 Social awareness: face detection, diarization, presence sensing 16:15 Hardware options: Linux, RTX 3090, DGX Spark, Raspberry Pi 18:25 The Reachy Mini Lite kit — and why it's better than building a drone 19:40 Where to find Alexis and join the Discord 20:10 One eye, four ears — Sparky's hardware explained 24:25 What OpenClaw enables that other frameworks don't 28:13 "Do you have a body, or are you a body?" — a live philosophical exchange 31:17 Live model switch: Claude Sonnet 4.6 → Nemotron 3 Super 33:01 The shared workspace principle — implicit shared attention 38:04 Orchestration in practice: Emacs, sub-agents, cross-platform 40:11 Cascading vs voice-to-voice architecture — the real tradeoff 42:15 Designing Sparky's voice (and the 1930s experiment) 44:12 What's genuinely useful day-to-day — two real examples 48:47 Nemotron 3 Super live — impressive, then the context window 53:38 The model Sparky was running before (Claude Sonnet 4.6) 54:03 Five years out: the future of personal AI companions 58:14 The closest thing to Jarvis I've ever seen 01:00:22 What's coming next — how fast the pieces are moving 01:02:16 Where to find Alexis and join the community ``` Links - Sparky project and Discord: https://myrobotSparky.com - Reachy Mini Lite: https://huggingface.co/reachy-mini The Private AI Lab is hosted by Johan van Amersfoort — Chief Evangelist and AI Lead at ITQ. 📬 Newsletter: https://www.linkedin.com/build-relation/newsletter-follow?entityUrn=7381951883810111489 📝 Blog: https://johan.ml 🔗 LinkedIn: https://www.linkedin.com/in/hojan

014 - Project Q9: Where Robotics and AI meet (with Sander Harrewijnen)

In this episode, Johan is joined by long-time colleague Sander Hardewijnen to pull back the curtain on Project Q9 — an ambitious internal project at ITQ that combines a Unitree Go 2 Pro robotic dog, private AI, computer vision, and modern cloud-native development practices. From gesture recognition trained on 30,000 hand images to a Skynet-obsessed dog posting on LinkedIn, this episode is a deep dive into what happens when you give great engineers a suitcase full of robot and say, "see where it goes." The conversation also covers the state of open-source AI coding assistants (OpenClaw vs NemoClaw), the realities of vibe coding in a production context, and what partner platforms like Red Hat OpenShift AI and SUSE AI actually enable beyond conversational AI. Sander's blog: https://harre.dev Q9's LinkedIn page: https://www.linkedin.com/in/q9-the-dog-2206863b1/ Chapters 00:00 Welcome & Introduction01:20 Icebreaker: Best AI Fail02:12 NemoClaw vs OpenClaw: Security & Sandboxing04:49 Running OpenClaw in an Isolated VLAN05:32 OpenClaw as a Personal Assistant: Home Assistant, News & Efteling API09:11 OpenClaw in the ITQ WhatsApp Group11:10 Introducing Project Q913:22 Why Robotics + Cloud-Native + AI?16:16 Technical Anatomy of Q918:30 Partner Platform Showcase: Broadcom, Red Hat & SUSE19:20 Debunking the GPU Myth23:05 Building the Gesture Recognition Model25:00 Training Progression: Epochs, Accuracy & Landmarks30:21 Hand Landmark Detection & the Gesture Pipeline32:34 Crowd Reactions at KubeCon33:57 Fine-Tuning vs Training From Scratch36:16 Use Case 2: Q9's LLM-Powered LinkedIn Persona40:41 Running LLMs on Partner Inference Platforms42:26 What's Next for Q9?43:44 Digital Twins in NVIDIA Omniverse + ROS245:10 Key Takeaways48:53 Responsible Vibe Coding49:58 Open-Sourcing Q9 — Coming Soon

013 - AI Resource Management Update & Tools with Frank Denneman

In this episode of The Private AI Lab, Frank Denneman returns as the first recurring guest to go deeper into one of the most misunderstood challenges in AI: 👉 Resource management for GPU workloads Building on our previous conversation, this episode shifts from why it matters to how to actually design it right. We dive into real-world challenges like GPU fragmentation, siloed capacity, and why traditional infrastructure thinking breaks down when AI enters the data center. Frank shares practical insights from his latest research, blog series, and tools—helping architects and platform engineers understand how to design efficient, scalable AI environments. 🔍 What you’ll learn in this episode * Why GPU workloads behave fundamentally differently from CPU/memory workloads * What GPU fragmentation really is (and why it kills utilization) * The difference between same-size vs mixed-mode placement * How placement IDs turn GPU scheduling into “Tetris” * Why “right-sizing” beats “perfect fitting” in AI environments * How to design a GPU profile catalog that actually scales * The role of state, agents, and storage in next-gen AI platforms 🔧 Tools & Resources mentioned Frank created practical tools to help you design and validate your GPU environments: * 👉 vGPU Silo Capacity Calculator https://frankdenneman.ai/tools/vgpu-silo-capacity-calculator/ * 👉 Same-size vs Mixed-mode Placement Tool https://frankdenneman.ai/tools/same-size-vs-mixed-mode/ * 👉 Deep dive on unified memory & modern AI workloads https://frankdenneman.ai/posts/2026-03-23-understanding-unified-memory-dgx-spark-nemoclaw-nemotron/ Chapters: 00:00 Intro — Frank Denneman returns 01:30 AI hype vs real engineering 03:00 DGX Spark, NemoClaw & local AI agents 10:30 From LLMs to agents & stateful systems 12:00 Why AI infrastructure is different 15:00 What is GPU fragmentation? 19:30 Same-size vs mixed-mode placement 23:00 GPU “Tetris” and placement IDs explained 27:00 Right-sizing vs perfect fitting 32:00 The tools: capacity & placement simulation 36:00 GPU silos vs stranded capacity 41:00 Model sizing, KV cache & dynamic usage 48:00 Future of AI: smaller models & orchestration 55:00 AI-assisted coding & real-world impact 59:00 Key lessons learned 01:02:00 Closing thoughts

012 - From Sepsis to Sovereign Cloud: OpenShift AI in Healthcare (with Vincent Tsugranes)

AI in healthcare didn’t start with ChatGPT. Long before generative AI, hospitals were using machine learning for sepsis detection, imaging diagnostics, and predictive analytics. In this episode of The Private AI Lab, Johan sits down with Vincent Tsugranes, Chief Architect at Red Hat, to explore what’s real, what’s hype, and why platform matters more than ever. They discuss: * Why 95% of AI projects fail * The evolution from OpenShift Data Science to OpenShift AI * Models-as-a-Service inside hospitals * vLLM vs LLMD for large-scale inference * Guardrails, hallucinations, and enterprise risk * Sovereign cloud and why healthcare is moving on-prem again * What “ambient AI” might mean in the next 12 months This episode is for architects, platform engineers, healthcare IT leaders, and anyone building private AI in regulated environments. 00:00 – Red lights & farming with AI 02:10 – The first AI spark moment 04:00 – When “AI” became AI (ChatGPT moment) 07:20 – Why 95% of AI projects fail 11:00 – Machine learning vs modern AI 13:30 – Platform vs point solutions 16:00 – The history of OpenShift AI 19:00 – What is OpenShift AI under the hood? 22:00 – Hardware enablement & NVIDIA 25:00 – vLLM explained 27:30 – LLMD and distributed inference 30:00 – Healthcare use cases (sepsis, imaging, insurance) 33:00 – Models-as-a-Service inside hospitals 36:00 – Guardrails & hallucination risks 39:00 – Observability & FinOps explosion 42:00 – OpenShift 5 and platform intelligence 44:30 – Sovereign cloud in healthcare 48:00 – The future: ambient AI & rising power bills