On NVIDIA DGX Spark, an out-of-memory error does not necessarily mean a dedicated GPU VRAM pool is full: the system uses unified DRAM shared by the GPU, CPU, and other compute engines. Start by identifying the exact stage that fails, then interpret memory readings in that shared-memory context and adjust the allocation that is actually under pressure.
1. Find the phase where the failure occurs
Save the full error and the surrounding application or container logs. Note whether it occurs during model loading, initialization or warm-up, CUDA graph capture, or steady-state execution. Different phases can stress different allocations, so the phase is a useful first diagnostic clue. NVIDIA’s NIM troubleshooting guide uses this phase-based approach; its specific settings and flags apply to NIM, not automatically to other model-serving tools or frameworks.
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- Model loading: Determine whether the failure happens while weights are being read or placed in memory.
- Initialization, warm-up, or graph capture: Look for a temporary peak during setup rather than assuming steady-state memory use is the cause.
- Steady-state execution: Record the request, batch, sequence length, or other workload conditions that coincide with the error, along with the logs.
2. Read memory figures as unified-memory estimates
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 compute engines, not a promise that one application can use all 128 GB. The amount available to a workload depends on other system use and its own allocation needs.
NVIDIA explains in its known-issues documentation that cudaMemGetInfo does not include DRAM that the CPU might reclaim by moving pages to SWAP. Its reported free-memory figure can therefore be lower than the amount that may eventually be allocatable. That is not a guarantee that a particular allocation will succeed: reclamation and swapping have performance costs, and a low reading remains useful context when investigating pressure.
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On iGPU platforms, NVIDIA says nvidia-smi may show Memory-Usage: Not Supported even while listing GPU memory by process, because these platforms do not have dedicated framebuffer memory. Treat that output as a reporting limitation, not evidence of unlimited capacity. See the DGX Spark known issues and the CUDA Programming Guide’s unified and system memory overview.
3. Match the remedy to the allocation that fails
There is no single OOM cause or universal fix established for every DGX Spark workload. Use the failing phase and the application’s own documentation to identify whether the pressure comes from model weights, temporary setup allocations, or ongoing workload state. A change to how memory is reported does not itself reduce what the workload needs.
If model loading fails
Check the model profile and precision selected by the application, and compare them with its documented memory requirements. NVIDIA’s NIM guidance says a weight-loading failure can indicate that the selected weights or precision do not fit the chosen configuration. This is a NIM example, not a universal DGX Spark diagnosis; consult the corresponding guidance for your serving stack.
If setup or CUDA graph capture fails
Check whether initialization or graph capture creates a temporary peak that exceeds the available headroom. In its NIM example, NVIDIA recommends leaving more memory unreserved or disabling CUDA graphs. Disabling graphs can reduce inference throughput, and NIM-specific options should not be copied into unrelated software.
If an established workload fails during execution
Compare the failing run with a successful one: note workload inputs and settings, application configuration, and the point in the logs where the failure begins. Use the framework or application documentation to reduce the allocation tied to that stage; do not assume a different memory meter reading has changed the workload’s actual demand.
4. Try the documented cache-flush workaround 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. If you choose to test it, preserve the original logs and workload configuration so you can compare runs:
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sudo sh -c 'sync; echo 3 > /proc/sys/vm/drop_caches'This is not a routine maintenance command or a guaranteed OOM fix. A changed result after the flush is diagnostic information, not proof that cache use was the sole cause or that the underlying allocation problem is resolved.
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Before comparing advice or escalating a failure, record whether the machine is a Founders Edition or a GB10 partner system, along with the installed OS, kernel, driver, CUDA, and framework versions. NVIDIA’s release notes list these Founders Edition versions for the release documented at the time of the page: 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, and SoC 2.155.11. Check the live release page and the machine itself for current versions; partner systems may receive updates on a different schedule.
NVIDIA’s July 2026 release notes say the included driver improved OOM handling and user feedback under memory pressure for Founders Edition. That release note does not establish the same update timing for GB10 partner systems.
6. Prepare a useful support report
If the error persists, provide enough context to distinguish a workload limit from a software or system issue. Include:
- The exact error and surrounding application or container logs.
- The failing phase and the workload conditions that trigger it.
- System variant and installed OS, kernel, driver, CUDA, and framework versions.
- Relevant model profile, precision, and memory-related configuration.
- Whether any diagnostic change altered the failure, and how the subsequent run compared.
NVIDIA’s DGX Spark User Guide is the starting point for system documentation. An individual OOM cause cannot be determined from the error label alone; the logs, workload, and installed software context matter.
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