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"Distinguished Engineer and Chief Technologist for AI at VMware Cloud Foundation. Writing about AI infrastructure, GPU platform design, and NUMA-aware architecture."

"Architecting AI Infrastructure Series - Part 11 A multi-GPU VM isn’t only asking for multiple devices. It’s asking for a specific communication geometry. This distinction matters. When a platform team provisions a VM for LLM inference or fine-tuning, they’re not simply allocating two units of compute. They’re allocating two GPUs that can communicate at hundreds of gigabytes per second over NVLink. Two GPUs on the same server that must communicate via PCIe won’t deliver the same result."

"Architecting AI Infrastructure Series - Part 10 Part 9 covered why it’s important to understand the topology when using multiple GPUs. When a model runs across several GPUs, communication between them becomes part of the process. Not all GPUs in a server communicate at the same speed, and these differences can impact performance. Many AI teams prefer to run their workloads on a single server. This helps reduce network complexity and simplify deployment. Still, there are several ways to set up multiple GPUs in a single server."

"NemoClaw became the talk of GTC 2026 within hours of its announcement. It wraps OpenClaw in NVIDIA’s OpenShell runtime, adds guardrails, and gives you an always on AI agent with a single install. Jensen Huang called OpenClaw the operating system for personal AI. NemoClaw is what makes that usable. This is part 4 of the AI Memory series and focuses on how memory behaves on real systems. I installed NemoClaw on a DGX Spark and ran Nemotron models locally to understand what actually happens in memory. The most important takeaway is simple. Unified memory breaks the usual GPU mental model."

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