/spark-memory-thermal-ops

name: spark-memory-thermal-ops
description: 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.

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 combination fit.

• A run OOMs mid-load or mid-step and the

remediation order matters — what to try first, second, third.

• Watching temperature and power during a

multi-hour job, deciding whether a slowdown is thermal throttling or something else.

• Planning to run a trainer alongside an

inference server (vLLM, Ollama) on the same box.

UMA Memory Model

Spark has no separate GPU VRAM — the GPU and CPU share one 128GB pool. Two consequences:

• **`nvidia-smi` and `cudaMemGetInfo`

underreport pressure — or report nothing at all.** Both report CUDA-allocator-visible memory, not the pool's actual state — a box can show headroom in `nvidia-smi` and still OOM, because page-cache and mmap'd pages the allocator doesn't see consume the same pool. On some driver/setups, the memory query returns `[N/A], [N/A]` outright instead of a number — a script grepping for a numeric value there gets nothing, not a misleading undercount (see `spark-training-gotchas` gotcha G3).

• **Model load is a transient peak, not the

steady state.** Loading safetensors weights mmaps the file, then copies into CUDA tensors — for a window during load, both the mmap'd pages and the CUDA copy count against the pool at once. A model that fits while training can still OOM during load if headroom was sized for the post-load footprint instead of this doubled transient.

Plan and diagnose with `free -g`, not `nvidia-smi`:

free -g | awk 'NR==2 {print "free:", $4, "GB"}'

Rule of thumb: take that free figure, subtract a few GB for OS/driver overhead, and budget against the result — not the 128GB spec number. The worksheet in `references/uma-accounting.md` accepts parameter count, dtype, and method as input, and returns a memory estimate to compare against known anchors.

Planning Sequence

Before launch, work through these in order:

1. Read `free -g`; subtract OS/driver overhead for the budget. 2. Estimate weights + optimizer + gradients + activations from `references/uma-accounting.md`. 3. Compare against the closest anchor (70B QLoRA, 27B LoRA, 9B full FT), not the estimate alone. 4. If the estimate is close to the budget, start with shorter packing or a smaller batch — cheaper than hitting the OOM Ladder mid-run.

Example: Sizing a 70B QLoRA Run

A sanity check of the worksheet formula against the ≈40GB anchor:

params = 70e9
weights_gb = params * 0.5 / 1e9      # NF4, step 1
adapter_gb = 0.5                     # step 5, negligible
total_gb = weights_gb + adapter_gb   # + activations
print(f"{total_gb:.0f}GB before activations")

Weights alone land near the ≈40GB anchor — a plan estimating far above that for the same model class is a signal to recheck dtype and method.

The OOM Ladder

When a job OOMs on unified memory, work this ladder in order. Each step is more disruptive than the last — don't skip ahead: **reducing batch size is never step 1.**

1. **Flush the buffer cache.** Page cache from a previous run or a large dataset read often accounts for GB of the "missing" headroom. This costs nothing but a rerun and doesn't touch the job's configuration:

   sync; echo 3 > /proc/sys/vm/drop_caches

Needs root; a between-run reset, not a mid-training step. See `spark-training-gotchas` (gotcha G3) for the full diagnostic behind this step.

2. **Reduce batch size or packing length.** Only after a flush fails to free enough headroom, cut batch size or packing length — the first step that changes what the run does. Prefer packing length first; it drives activation footprint more directly at long context.

3. **Downgrade the method: bf16 LoRA before QLoRA.** If flushing and shrinking batch/pack still OOM, drop the method a tier — bf16 LoRA is next, not the reverse. QLoRA's bitsandbytes dequantization buffers are transient CUDA-side allocations that can OOM before an equivalent bf16 LoRA run would, even though QLoRA's steady-state footprint is smaller. A QLoRA OOM is not proof the model doesn't fit.

Fall back further (smaller model, multi-Spark) only after all three steps and the job still won't fit.

Thermal Monitoring

Multi-hour runs push into Spark's sustained power ceiling, well under the rated figure — expected platform behavior, not a symptom to explain away:

• Sample temperature and power alongside the

training logs, not after a slowdown is noticed — every 3