ai-infrastructure
In Defense of the DGX Spark: A Reality-Grounded Take
NVIDIA marketed the DGX Spark as a "supercomputer on your desk," and taken literally that sets you up to be let down: its 273 GB/s memory bandwidth means slow token generation on dense models, which is why a wave of early buyers returned or resold theirs. But that critique measures the machine against the wrong job. The Spark is a large-memory device, not a fast-dense-model device. It comes alive on Mixture of Experts models, on clustering multiple units over ConnectX-7 to run near-frontier-scale open weights like Qwen3.5-397B at home, and on prefill-heavy agentic workloads where its Blackwell compute chews through long prompts far faster than a Mac Studio.
The Intern that Doesn't Leave the Building
AI security is about where the AI lives, what it can access, and who controls its actions. This article argues that private and local AI offer a more practical path for serious organizations by keeping sensitive data inside trusted boundaries. The result is not weaker or less ambitious AI, but AI that can safely work with the information that matters most. Private AI makes powerful systems more governable, auditable, and useful.
Private AI Is Not Just About Privacy: It’s About Cost, Consistency, and Control
Private AI is not just about keeping data private. For serious AI users, local LLMs matter because of cost, consistency, control, open-weight models, and the limits of hosted AI subscriptions.
