On NVIDIA DGX Spark, an out-of-memory error does not necessarily mean a discrete GPU has run out of dedicated VRAM. The GPU shares system DRAM with the CPU and other compute engines, and NVIDIA cautions that cudaMemGetInfo can report less free memory than may eventually be allocatable through SWAP reclamation. Find the exact workload stage that fails, then choose a remedy that addresses that allocation rather than relying on one memory reading.
Start by locating the failure
Save the complete error message and the surrounding application or container logs. Record whether the failure occurs while loading model weights, during initialization or warm-up, during CUDA graph capture, or during steady-state execution. These phases can place different demands on memory, so the right fix depends on when the allocation fails.
NVIDIA’s NIM memory troubleshooting guide uses this phase-based approach. Its specific settings and flags apply to NIM, not automatically to other serving stacks or CUDA applications.
Read memory reports in the context of unified memory
NVIDIA lists 128 GB of LPDDR5x unified system memory for the documented DGX Spark configuration in its hardware overview. This is system memory shared among the GPU, CPU, and other compute engines—not 128 GB of dedicated GPU memory guaranteed to be available to one application.
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NVIDIA’s Known Issues documentation says cudaMemGetInfo may show less memory than is ultimately allocatable because the CPU may reclaim additional DRAM pages by moving them to SWAP. A low reported free-memory value is therefore not, by itself, a complete capacity verdict. But a higher eventual allocation is not guaranteed, and swapping or reclaiming memory can carry performance costs.
On iGPU platforms, NVIDIA says nvidia-smi may display Memory-Usage: Not Supported even when it lists GPU memory by process. The platform has no dedicated framebuffer memory, so this reporting behavior is expected; it does not indicate unlimited headroom.
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Match the remedy to the allocation that fails
Model weights fail to load
For model-serving software, check the selected model profile and precision against that software’s memory guidance. In its NIM example, NVIDIA says a weight-loading failure can indicate that the chosen weights and precision do not fit the selected configuration. Treat that as a NIM-specific diagnostic, not a universal rule for every framework.
Initialization or CUDA graph capture fails
Some workloads need temporary memory during initialization, warm-up, or graph capture in addition to the memory used by model weights. For a NIM graph-capture failure, NVIDIA’s example recommends leaving more memory unreserved or disabling CUDA graphs. Disabling graphs can reduce inference throughput; do not apply NIM flags to another stack unless its documentation supports them.
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Steady-state execution fails
For an error that appears only after execution begins, inspect the workload’s ongoing allocations and the point at which they grow. Compare the failing run with the same configuration before the error, including the relevant logs and workload settings. The official DGX Spark guidance does not establish one universal cause or a tested fix for every framework, so avoid assuming that a model-load remedy will resolve a runtime allocation failure.
Try cache flushing only as a debugging step
NVIDIA’s DGX Spark porting guide documents flushing the buffer cache as a debugging workaround, followed by restarting the application:
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sudo sh -c 'sync; echo 3 > /proc/sys/vm/drop_caches'This is not a guaranteed correction or a routine fix for recurring OOM errors. Preserve the original logs and workload configuration, then compare the result of a repeat run to determine whether the workaround changed the failure.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Record the system variant and software versions
Before comparing advice or reporting the problem, note whether the machine is a Founders Edition or a GB10 partner system, along with its OS, kernel, driver, CUDA, and framework versions. NVIDIA’s release notes list the following Founders Edition versions at the time of the July 2026 release:
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| Founders Edition component | Version listed by NVIDIA |
|---|---|
| DGX OS | 7.5.0 |
| NVIDIA GPU driver | 580.159.03 |
| CUDA Toolkit | 13.0.2 |
| Canonical kernel | 6.17 |
| UEFI | 1.110.13 |
| EC | 3.5.8 |
| USB PD | 0.5.22 |
| TPM | 7.516.1 |
| SoC | 2.155.11 |
NVIDIA’s July 2026 release notes say the included driver improved OOM handling and user feedback under memory pressure for Founders Edition. The listed versions and update timing should not be assumed to match GB10 partner systems; check the live release notes and the versions installed on the affected machine.
What to include when escalating an unresolved error
If the same failure persists, a useful report should let someone identify both the allocation context and the software environment. Include:
- The full error and nearby application or container logs.
- The failing phase: model load, initialization or warm-up, CUDA graph capture, or steady-state execution.
- The system variant and installed OS, kernel, driver, CUDA, and framework versions.
- The model or workload configuration, including profile and precision where relevant.
- Any change already tried and whether the failure moved, disappeared, or stayed the same.
NVIDIA’s DGX Spark User Guide is the starting point for system-specific documentation. For general CUDA memory concepts, see NVIDIA’s CUDA Programming Guide section on unified and system memory.
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