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First identify where the failure occurs: while loading a model, during inference, in a vLLM server, or during training/fine-tuning. The remedy depends on the stage and the workload. DGX Spark has 128 GB of unified system memory shared by CPU and GPU, not a separate 128 GB pool reserved entirely for model weights. Context, concurrent requests, caches, auxiliary components, and other system work all affect whether a run fits.
Record the exact error, model and quantization, context length, batch size or concurrent sequence count, and whether the process reports “CUDA out of memory” or is killed. Then use the matching checks below.
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Start by locating the failure
“Model load fails – CUDA out of memory,” an OOM during active inference, a vLLM startup failure, and “Out of memory during training” point to different parts of the workload. NVIDIA troubleshooting distinguishes CUDA OOM messages from processes killed when the system runs out of memory.
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- Note the application and stage: model load, inference, server startup/request handling, or training.
- Capture the full error text and logs, including whether the process exited with an error or was killed.
- Write down the model identifier, precision or quantization, context limit, batch size, and number of simultaneous sequences.
- Record your DGX OS and driver versions before comparing behavior with release notes.
These details help separate a model that cannot be loaded from one that loads but exceeds available memory when context or concurrency rises.
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“Model load fails – CUDA out of memory”
If memory runs out while loading or initializing a model, start with its runtime footprint rather than parameter count alone. NVIDIA’s multimodal inference troubleshooting recommends trying a smaller model or a quantized version such as FP8 or FP4 when the model does not fit. Confirm that the specific framework and model support the chosen precision; quantization availability and compatibility are not universal.
Weights are only one part of a running model’s memory needs. Inference also uses memory for context and KV cache, and some configurations include additional model components. A model can therefore load at one context or configuration and fail at another.
OOM in vLLM: reduce context or concurrency
NVIDIA’s vLLM troubleshooting guidance identifies an oversized model or excessive context as common causes. Context length covers the prompt plus generated output; increasing it means reserving more memory for the KV cache. More simultaneous sequences also increase memory demand.
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- Reduce
--max-model-lento the longest prompt-plus-output context your application actually needs. - Reduce
--max-num-seqsif the server does not need to handle as many simultaneous sequences. - If memory pressure remains, adjust
--gpu-memory-utilizationas appropriate for the configuration. NVIDIA’s serving example uses0.8to leave headroom; this is an example, not a guaranteed optimum for every Spark workload. The guide discusses raising the setting toward0.95for a dedicated GPU to fit more KV cache, but that should not be treated as a universal Spark recommendation.
When comparing configurations, consider model and quantization footprint, maximum context, sequence count, KV-cache demand, utilization setting and remaining headroom, as well as the latency and quality your application requires. Change one or two constraints at a time so you can identify what removes the failure without imposing unnecessarily tight limits.
OOM during training or fine-tuning
For “Out of memory during training,” NVIDIA’s NeMo troubleshooting guidance lists three options to investigate in the relevant training stack:
- Reduce the batch size.
- Enable gradient checkpointing.
- Use model parallelism.
These options make different trade-offs, and no single one is established as best for every model or run. Check the training framework’s configuration and compatibility requirements before changing a setting.
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Understand memory readings on unified memory
DGX Spark’s 128 GB is unified system memory used by both CPU and GPU activity. It is not equivalent to a dedicated graphics card with a separate, fixed framebuffer pool. The operating system and CPU-side applications also need memory, so the full advertised capacity should not be treated as available to one GPU job.
NVIDIA notes that nvidia-smi memory usage may show “Not Supported” on iGPU platforms, and UMA-related fields in vLLM troubleshooting may show N/A. NVIDIA also explains that cudaMemGetInfo can undercount memory that the operating system may reclaim by moving pages to system swap or releasing page cache. A single GPU-memory field therefore may not tell you whether a workload is supportable; use the error, application behavior, and overall system pressure together.
Memory pressure within capacity: cache-flush workaround
For certain UMA memory-pressure cases where a workload appears to be within capacity, NVIDIA documents flushing the buffer cache and then restarting the application. The command is:
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sudo sh -c 'sync; echo 3 > /proc/sys/vm/drop_caches'
This is a privileged, system-level workaround for the documented cache-pressure case, not a routine first step or a way to make an oversized model fit. It does not reduce the workload’s memory requirements or establish that the model and configuration are otherwise supportable.
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Check software release context
NVIDIA’s DGX Spark Founders Edition release notes list DGX OS 7.5.0, driver 580.159.03, CUDA Toolkit 13.0.2, and kernel 6.17, and report a July 2026 improvement to OOM handling with user feedback under memory pressure. Check the current notes and your installed versions before attributing an OOM to that change: NVIDIA cautions that GB10 partner systems may receive updates on a different schedule.
Why parameter count alone can mislead
NVIDIA’s speculative-decoding example says Qwen3-235B-A22B exceeds what one Spark can hold in the cited configuration, even with FP4: the model weights, KV cache, and Eagle3 draft head together exceed 128 GB. This is a specific workload example, not a rule that all models of a given parameter count either fit or fail. The useful check is the memory footprint of the complete runtime configuration, including caches and auxiliary components, rather than weights in isolation.
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