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.
Profile →npx skills add wshobson/agents --skill spark-memory-thermal-ops --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 the job starts. ## Common Issues Quick Reference | Situation | Do this | |---|---| | Planning headroom before launch | Budget against `free -g`, not `nvidia-smi` — see UMA Memory Model | | Job OOMs on unified memory | Work the OOM Ladder in order: flush, then batch/pack, then method downgrade | | Throughput drops mid-run | Check the power/temp log before assuming a config bug — see Thermal Monitoring | | Trainer + inference server both wanted | Run one at a time — see Concurrent Workloads | ## When to Use This Skill - Sizing a training run against the 128GB pool before launch — will this model, method, and batch/pack combinat
- Common Issues Quick Reference
- When to Use This Skill
- UMA Memory Model
- Planning Sequence
- Example: Sizing a 70B QLoRA Run
- The OOM Ladder
- Thermal Monitoring
- Concurrent Workloads
free -g | awk 'NR==2 {print "free:", $4, "GB"}'
bash assets/thermal-sample.sh 30 thermal.log
ps aux | grep -E 'vllm|ollama|trl|axolotl' | grep -v grepWhat does the spark-memory-thermal-ops skill do?
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.
How do I install it?
Run `npx skills add wshobson/agents --skill spark-memory-thermal-ops --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From wshobson/agents, a repository with 38,479 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.