Individual skill
spark-memory-thermal-ops
Manage unified memory and thermals during long-running ML jobs on NVIDIA DGX Spark. Use when planning memory headroom for a training run on GB10, when a job OOMs on unified memory, or when monitoring temperature and power during multi-hour training.
plugins/dgx-spark-ops/skills/spark-memory-thermal-ops/SKILL.md
Source commit 4bde3168b43c0655c4d00ea2726e631719908a6d
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- Last checked
- Nov 7, 2026
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- License
- MIT
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SKILL.mdcore · 7,980 bytesSKILL.mdcore · 7,980 bytesSKILL.mdcore · 7,980 bytesSKILL.mdcore · 7,980 bytesSKILL.mdcore · 7,980 bytesSKILL.mdcore · 7,980 bytesSKILL.mdcore · 7,980 bytesassets/thermal-sample.shruns · 1,017 bytesreferences/uma-accounting.mddoc · 4,407 bytes
Read excerpt
# Spark Memory & Thermal Ops
DGX Spark's GB10 chip has one 128GB unified
memory (UMA) pool shared by CPU and GPU, and a
sustained power ceiling well below its rated
figure. Both break discrete-GPU assumptions:
headroom isn't what `nvidia-smi` reports, and a
run that starts fast will slow down mid-job
with nothing misconfigured. This skill covers
planning memory headroom, working an actual
OOM, and watching thermals across a long job.
For launch-time failure modes (ABI mismatches,
flash-attn, playbook breakage), see
`spark-training-gotchas` — this skill assumes