Spark model scheduler

DGX Spark has one ~121 GB pool for the OS, every LLM's weights and KV cache, and every diffusion model. A service that loads fine alone can kill another one later. Decide the budget first, then start things.

Workflow

1. Measure before acting:

   cinefoundry mem status
   

   It reports total and available unified memory, per-container memory, and ComfyUI's loaded-model state. nvidia-smi shows no total on GB10; trust /proc/meminfo. Per-container numbers cover host allocations only: CUDA memory is not charged to the container's cgroup, so a vLLM or SGLang container holding 100 GB can show under 5 GB. Use the drop in available memory, not docker stats, to size a GPU service.