Set up a working ML training/inference environment on NVIDIA DGX Spark (GB10, aarch64, CUDA 13). Use when installing PyTorch/Unsloth/TRL/vLLM on DGX Spark, hitting libcudart or wheel-ABI errors on aarch64, or choosing between NGC containers and bare pip installs.
Set up a working ML training/inference environment on NVIDIA DGX Spark (GB10, aarch64, CUDA 13). Use when installing PyTorch/Unsloth/TRL/vLLM on DGX Spark, hitting libcudart or wheel-ABI errors on aarch64, or choosing between NGC containers and bare pip installs.
Spark Environment Setup
DGX Spark ships a GB10 Grace Blackwell chip: aarch64 CPU, SM121
GPU, 128GB unified memory, CUDA 13. This is a narrower and
younger platform than a standard x86 CUDA 12 box, so package
selection and ABI matching matter more than usual — the wheel
ecosystem for aarch64 + CUDA 13 is still filling in.
When to Use This Skill
Setting up a fresh Spark box for training or inference.
Hitting an import error mentioning libcudart, a missing
symbol, or a wheel that "installed fine but won't load."
A framework install (PyTorch, Unsloth, TRL, vLLM, xformers)
fails, hangs, or silently falls back to CPU.
Deciding whether to use an NGC container or bare pip.
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Restoring a working setup after an OS reinstall or a
base-image update, needing to re-verify from scratch.
Each of these accepts the same general fix: match the
container/wheel combination to CUDA 13 and SM121, don't fight
the ABI.
Container-First Rule
Quick decision, before the detail below:
Standard training/inference work → NGC PyTorch container.
Unsloth-centric fine-tuning → Unsloth container (it ships
the pinned Triton/xformers/transformers combination already
validated for that path).
Neither fits (custom system package, local IDE interpreter)
→ bare pip, following the exact sequence further down.
Default to a container. Use nvcr.io/nvidia/pytorch:25.09-py3
as the base for general work — the newest tag confirmed working
on this hardware; pull a newer blessed tag if locally available
rather than hard-blocking on 25.11-py3. NGC's tag is dated, so
running it directly is fine:
docker run --runtime=nvidia --gpus all -it --rm \
nvcr.io/nvidia/pytorch:25.09-py3
unsloth/unsloth:dgxspark-latest is a moving tag by
contrast — resolve and pin its digest before running it for
anything reproducible; the bare tag is a discovery step only,
not the default invocation. Full pull-inspect-pin sequence and
flag rationale/volume mounts for finetuning/ run dirs:
references/container-workflow.md. Treat bare pip as the exception.
The reason for the container-first stance is pinning, not
convenience. Triton, xformers, and transformers versions
interact narrowly with GB10's SM121 target and CUDA 13; a
container locks all of them together against a combination
already validated on this hardware. Bare pip leaves that
resolution to you, one broken import at a time.
When bare pip is warranted, follow the NVIDIA playbook's
install sequence verbatim and in order:
The second command's --no-deps flag is not optional —
letting pip re-resolve Unsloth's dependency tree on aarch64 is
a common way to pull in an incompatible torch or triton build.
The third line is not optional either: the NGC base image's
bundled torchao is too old for current peft's LoRA-attach
path (ImportError: ... torchao ... only versions above 0.16.0 are supported) — a hard blocker, not a warning. Every == pin
above is load-bearing, taken from the dated known-good version
matrix in references/stack-matrix.md (its Last verified date
governs staleness) — an unpinned install resolves current PyPI
versions well outside what this Unsloth release supports.
Pull a fresh tag when a new blessed release is announced.
Rebuild locally from one of the two bases only when a project
needs an extra system package layered in — not to "upgrade" a
component the image already pins. Details on both paths:
references/container-workflow.md.
One more preflight: official DGX Spark playbooks have shipped
broken before. Check recent issues on
github.com/NVIDIA/dgx-spark-playbooks (and the other
resources in references/stack-matrix.md) before trusting a
recipe verbatim for a long run.
The ABI Rule
The single most common failure on Spark is a CUDA 12/13 ABI
mismatch: a wheel built against libcudart.so.12 loaded on a
system that only has libcudart.so.13. The install usually
succeeds; the failure surfaces later as a missing-symbol error
or a segfault that doesn't obviously point at CUDA.
Fix: pull wheels from download.pytorch.org/whl/cu130 (the
cu130-tagged aarch64 builds), or use one of the containers
above, which already carry a matched build. Before chasing a
stack trace that mentions a CUDA symbol, check which CUDA tag
the installed wheel was built against:
If that output doesn't start with 13, the ABI mismatch is the
first thing to fix. NGC container builds (e.g.
nvcr.io/nvidia/pytorch:25.09-py3) build torch internally
against CUDA 13 with no +cu130 wheel tag — pip show torch
won't say cu130 there, and that absence alone is not a failure.
Typical symptoms:
ImportError: undefined symbol referencing a CUDA runtime
function.
A segfault on the first .cuda() call, no useful traceback.
A wheel that installs cleanly, then fails at import time —
pip's resolver doesn't check CUDA ABI, only version constraints.
Two "identical" environments behaving differently — usually one
has a cu130 wheel, the other a cu121/cu124 leftover.
The fix is the same regardless of symptom: match the wheel's
CUDA tag to the system, or use a container that already does.
Component Quick Table
Condensed status for the components most likely to come up.
Full table with wheel URLs, build flags, the sm_121 vs sm_121a
distinction, and the dated known-good version matrix:
references/stack-matrix.md.
Component
Status
PyTorch
✅ official cu130 aarch64 wheels
bitsandbytes
✅ works out of the box
Triton
✅ needs the TRITON_PTXAS_PATH parameter set
flash-attn
❌ skip pip build; NGC bundles a working one — see spark-training-gotchas G2
xformers
source build only (TORCH_CUDA_ARCH_LIST=12.1)
vLLM
nightly wheels only
TransformerEngine / NVFP4 train
container-only
Everything else — Unsloth, Axolotl, TRL, PEFT — installs
cleanly through the container-first path above. LLaMA-Factory
and NeMo are fragile on Spark; check upstream issues first.
Verification Commands
Confirm the environment can actually see the GPU before
running anything expensive:
This call returns two values; the exact output format is one
line, <bool> <cuda-version>:
True 13.0
If it prints False instead, don't jump straight to a wheel
reinstall — ABI mismatch is one cause among several:
Hypothesis
Quick check
Runtime/flags
nvidia-smi fails in-container too
Device visibility
echo $CUDA_VISIBLE_DEVICES
Permissions
ls -l /dev/nvidia*
CUDA init state
wedged process; retry fresh shell/container
ABI mismatch (usual culprit)
torch.version.cuda not 13.x
Check nvidia-smi first — if it doesn't show the GPU, it's one
of the first three, not ABI. Reinstall a wheel only once ABI is
confirmed. Per-hypothesis detail: references/stack-matrix.md.
Run right after the container starts, before installing
project-specific packages.
One more check: if Triton kernel compilation fails once
training starts, set
TRITON_PTXAS_PATH=/usr/local/cuda/bin/ptxas and retry — see
references/stack-matrix.md for the full workaround list.
Next Steps
A verified environment is only the starting point. See also:
spark-training-gotchas for failure preflights before a
training run, and spark-memory-thermal-ops for unified-memory
OOMs and thermal throttling during long ones.
