From Ollama to vLLM
From 24GB Blackwell workstation cards to one 128GB DGX Spark, then two Sparks and a QSFP cable: running DeepSeek-V4-Flash-Vision-Exp locally, understanding vLLM, and planning the next step with a 200Gb/s switch.
Thoughts on technology, infrastructure, and AI
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From 24GB Blackwell workstation cards to one 128GB DGX Spark, then two Sparks and a QSFP cable: running DeepSeek-V4-Flash-Vision-Exp locally, understanding vLLM, and planning the next step with a 200Gb/s switch.
A DGX Spark with 128GB of unified memory let me explore much larger local models. Then I discovered Heretic variants, and a hardware experiment became a rather uncomfortable lesson in how AI refusals work, how easily they can be changed, and what that means for the rest of us.
I wanted to know whether a sub-$3k Australian workstation GPU could run useful modern local LLMs. The short answer is yes, if you pick the right model, the right quant, and build around the 24GB VRAM limit instead of pretending it is a tiny cloud.