run-ai-locally
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.
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.
