/spark-training-gotchas

name: spark-training-gotchas
description: Preflight and diagnose the ten known failure modes for ML training on NVIDIA DGX Spark. Use when a training run on DGX Spark fails to start, OOMs below the 128GB limit, slows down mid-run, or before any multi-hour training job on GB10.

Spark Training Gotchas

DGX Spark's GB10 chip (Grace Blackwell, SM121, 128GB unified memory, aarch64) has ten recurring failure modes across launch, memory, thermals, bandwidth, and precision. Each is named G1–G10 so it can be checked by number — the numbering is load-bearing for tooling that runs these checks. Read this before a long run, not after hour six.

When to Use This Skill

• A training run fails to start, with an import error or a

segfault that doesn't point at the real cause.

• A run OOMs while `nvidia-smi` still shows headroom.
• Throughput degrades partway through a run that started fine.
• Before any multi-hour or multi-epoch job on GB10.
• Wiring two Sparks together, before picking a parallelism

strategy.

• Choosing between FP8 and NVFP4 for a Spark-hosted run.

Common Issues Quick Reference

| # | Symptom | Fix | |---|---|---| | G1 | undefined symbol / segfault | cu130 wheel or container | | G2 | flash-attn wrong backend used | skip pip build; monkeypatch on NGC | | G3 | OOM despite headroom | drop page cache | | G4 | throughput drop / reboot | expect ~100W sustained cap | | G5 | memory-bound step slow | budget 180–192 GB/s | | G6 | cache evicted mid-run | one GPU server at a time | | G7 | NVFP4 slower than FP8 | stay FP8 unless `sm_121a` | | G8 | playbook fails outright | check upstream issues | | G9 | env breaks after install | use a container | | G10 | 2-Spark TP hangs | DDP/FSDP only, never TP |

The Ten Gotchas

G1: CUDA 12/13 ABI Mismatch

• **SYMPTOM:** `ImportError: undefined symbol` naming a CUDA

function, or a segfault on the first `.cuda()` call.

• **CAUSE:** most PyPI wheels link `libcudart.so.12`; Spark

ships CUDA 13. pip never checks CUDA ABI, so it surfaces only at import or first kernel launch.

• **CHECK:** `references/gotcha-checks.md` G1 — the wheel's

CUDA build tag.

• **FIX:** reinstall from `download.pytorch.org/whl/cu130` or

use a matched container.

G2: flash-attn — Skip the pip Build, Watch Unsloth's Auto-Detect

• **SYMPTOM:** `pip install flash-attn` still fails/hangs.

Unsloth may also silently train flash-attn over an explicitly requested SDPA.

• **CAUSE:** no aarch64/sm_121 wheel for bare pip — but NGC

containers ship a working SM121 flash-attn, and Unsloth auto-prefers it, dropping `attn_implementation="sdpa"`.

• **CHECK:** `references/gotcha-checks.md` G2 — is flash-attn

already present and working.

• **FIX:** bare pip — skip flash-attn, use SDPA (unchanged). On

NGC — the only reliable override is the monkeypatch in `references/gotcha-checks.md` G2.

G3: UMA OOM Below 128GB

• **SYMPTOM:** OOM during model load/training while

`nvidia-smi` still reports free memory under the 128GB cap — or, on some setups, `[N/A]` outright instead of a number.

• **CAUSE:** mmap and the CUDA allocator double-count pages

during safetensors load; QLoRA can OOM *earlier* than bf16 since dequantization adds transient allocs.

• **CHECK:** `references/gotcha-checks.md` G3 — read `free -g`

and `/proc/meminfo`, not `nvidia-smi`.

• **FIX:** drop the page cache with

`sync; echo 3 > /proc/sys/vm/drop_caches` — needs root, a between-run reset, not a mid-training step.

G4: Thermal Throttling

• **SYMPTOM:** throughput drops partway through a multi-hour

run, or the box spontaneously reboots under sustained load.

• **CAUSE:** sustained power draw caps around 100W versus the

240W rated figure; long runs push into that ceiling and throttle or, sometimes, reboot.

• **CHECK:** `references/gotcha-checks.md` G4 — sample

`nvidia-smi --query-gpu=temperature.gpu,power.draw`.

• **FIX:** if power plateaus under 240W while temperature

climbs, treat throttling as the cause; improve cooling or cap run length.

G5: Bandwidth Ceiling

• **SYMPTOM:** memory-bound workloads, decode-heavy RL loops

especially, plateau well below expected throughput.

• **CAUSE:** 273 GB/s is a spec ceiling, not sustained;

measured bandwidth runs 180–192 GB/s.

• **CHECK:** `references/gotcha-checks.md` G5 — observed step

time vs. the measured range, not spec.

• **FIX:** budget throughput from 180–192 GB/s; revise a plan

built on the 273 GB/s figure.

G6: Global UMA Resource Contention

• **SYMPTOM:** a process's KV cache/weights get evicted

mid-run silently, no OOM in its own logs.

• **CAUSE:** unified memory is

one global pool; an uncapped or near-capacity process competes with anything else and can evict it. A small, bounded workload doesn't — a <4GB LoRA coexists fine alongside vLLM capped at `gpu-memory-utilization<=0.5`.

• **CHECK:** `references/gotcha-checks.md`

G6 — other GPU-resident processes and whether capped.

• **FIX:** the one-heavy-job

rule applies to **uncapped or near-capacity** workloads — cap or stop unrelated servers first. A small, capped workload need not stop.

G7: NVFP4 Slower Than FP8 on SM121

• **SYMPTOM:** switching an inference workload from FP8 to

NVFP4 on Spark makes it slower, not faster.

• **CAUSE:** SM121 lacks `cvt.e2m1x2` unless kernels target

`sm_121a`; NVFP4 runs ~32% slower without it.

• **CHECK:** `references/gotcha-checks.md` G7 — capability

reports `(12, 1)`; does the build target `sm_121a`?

• **FIX:** stay on FP8 unless the build targets `sm_121a`.

G8: Stale Official Playbooks

• **SYMPTOM:** following an official DGX Spark playbook still

fails, with no local misconfiguration explaining it.

• **CAUSE:** official playbooks have shipped broken before;

the stack moves faster than the docs.

• **CHECK:** `references/gotcha-checks.md` G8 — the playbook

repo's recent issues.

• **FIX:** check `github.com/NVIDIA/dgx-spark-playbooks` issues

before