I’ve had a lot of conversations with people who want to finance and trade compute as an infrastructure asset. One question I keep coming back to: once you’ve financed a GPU server for five to ten years, how do you actually know what you own—and what condition it’s in—throughout those five years? How do you independently verify which physical GPUs and components are actually there? How do you know those servers are being properly operated and maintained when they may sit in a data center thousands of miles away? And how do you know the equipment you financed on Day 1 is still performing as expected on Day 1,000? Physical inspection doesn’t scale. Self-reporting isn’t enough. You need third-party verification. That’s what we’re building with SiliconMark. We work with infrastructure providers to track machines down to component-level UUIDs—GPU, CPU and the broader system—and build a persistent identity and performance history for the asset. You can know what the machine is, its expected depreciation curve, how it is actually performing, its thermal behavior and quality history, with timestamped records over its lifecycle. And because our tests are open-sourced, the results are reproducible and independently verifiable. Think of it as a digital service record for compute infrastructure, maintained by an independent third party. For equipment financing, knowing the original purchase price isn’t enough. You need to continuously know what the asset is, that it exists, how it has been treated, how it is performing, and ultimately what it is worth. If GPUs are going to become a financeable and tradable institutional infrastructure asset class, this verification layer is a fundamental building block. Third-party verification is the trust layer between the physical GPU and the financial asset.

Huge: NVIDIA just put a gaming PC, an AI workstation, and a GPU server into a laptop thin enough to disappear in your bag 😳 For 30 years, the PC has been the same thing: Intel or AMD inside, GPU on the side, and hope it doesn't crash. At Computex, Jensen Huang showed off RTX Spark: an ARM-based AI PC chip built around a Blackwell RTX GPU, a 20-core Grace CPU, and 128GB of unified memory. And it fits inside a 14mm laptop 🤯 On stage, it ran Forza Horizon 6 and 007 First Light at 100 FPS in 1440p. On battery. On Windows. Without throttling. The crazy part? It can run 120 billion parameter AI models on-device. No cloud. No API. No subscription. That means your AI agent no longer has to live in someone else’s data center. It can live on your machine. Always available. Private by default. Yours alone. This is the shift NVIDIA is really betting on: the laptop stops being a thin client for cloud AI and becomes a personal AI workstation. For developers, founders, analysts, designers, and finance teams, that could change the entire workflow. The PC used to be a screen with a keyboard. Now it is becoming the place where your AI actually lives.

$SANM just added Shanker Trivedi to its board. He spent 17 years at $NVDA and most recently ran its Enterprise Business, including worldwide data center sales. A board appointment doesn't change my model but considering where Sanmina is trying to go, I struggle to think of many backgrounds that fit better. For those who haven't followed my Sanmina posts, the reason I find the company interesting is pretty simple. Sanmina has been moving deeper into AI infrastructure and complex data center systems, while still trading much more like a traditional electronics manufacturer. The ZT Systems manufacturing business gave them another big step into that market and a much closer seat to the GPU server buildout. Adding someone who spent 17 years at Nvidia, with direct experience in data center sales and the requirements behind these systems, fits that direction very well.