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To run an LLM on NVIDIA DGX Spark, choose a model-specific Spark recipe and follow it with NVIDIA NIM, vLLM, or CUDA-enabled llama.cpp. First confirm the model image or format is supported, then account for memory used by its weights, context and runtime—not just parameter count. The official workflows below show how to launch a service and check it with a local request.
What DGX Spark can run—and what its memory figures mean
NVIDIA lists DGX Spark with 128 GB of unified memory, a 20-core Arm processor, 273 GB/s memory bandwidth, and up to 1,000 TOPS at FP4 with sparsity. These are vendor-published specifications, not independent benchmark results. NVIDIA describes support for models up to 200 billion parameters on one Spark and 405 billion across a dual-Spark configuration. Those ceilings do not guarantee that any particular model, precision, context length, or serving workload will fit. Memory demand also depends on weights and format, context and KV cache, runtime overhead, and other system use. See NVIDIA’s DGX Spark hardware overview.
Use a recipe for the specific model and runtime rather than choosing by parameter count alone. A NIM must have a Spark-compatible image or profile; vLLM and llama.cpp also have model- and configuration-specific requirements.
Choose a serving route
| Route | Best starting point | Model and setup considerations |
|---|---|---|
| NVIDIA NIM | A prebuilt, supported containerized inference service | Model-specific Spark image/profile and registry access must be confirmed. NVIDIA’s playbook starts with Llama 3.1 8B Instruct and links to other recipes. |
| vLLM | A model with a current DGX Spark vLLM recipe | Use its container configuration, GPU access, shared IPC, cache mount, and feasible maximum model length and GPU-memory utilization settings. |
| llama.cpp | A compatible GGUF checkpoint | Build with CUDA to use the GPU, then run llama-server. NVIDIA’s example uses quantized Qwen3.6-35B-A3B MTP; compatibility guidance is not a guarantee for every GGUF variant. |
The official material documents setup paths, not a controlled comparison of speed or concurrent-user capacity. Select based on the recipe, model format, and endpoint workflow you need; measure performance with your own workload if it matters.
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NVIDIA NIM
NVIDIA’s DGX Spark NIM playbook walks through registry authentication, launching a supported LLM NIM with Docker, and validating its OpenAI-compatible HTTP endpoint. Check NVIDIA’s NGC guidance for the model-specific Spark image or profile and current registry requirements. Not every NIM has a Spark variant.
vLLM
Follow NVIDIA’s vLLM instructions for DGX Spark for a single-node starting configuration. The example uses a Docker container, GPU access, shared IPC, a Hugging Face cache mount, and configured model-length and GPU-memory-utilization values. Choose values appropriate to the model and available memory; NVIDIA’s Spark-specific notes flag unified-memory pressure and point to troubleshooting guidance.
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llama.cpp
NVIDIA’s llama.cpp playbook describes building with CUDA, obtaining a GGUF checkpoint, and starting llama-server with an OpenAI-compatible chat-completions API. The playbook says GGUF models can be used when system memory is available to host and run them; check the exact checkpoint and its requirements rather than treating that as a blanket fit guarantee.
Set up and send a first request
- Complete first boot and update. Follow NVIDIA’s first-boot guide, install current updates, and connect the system to your network. NVIDIA supports local-console or network access after setup.
- Select the exact model recipe. Confirm its DGX Spark compatibility, model format, container image and tag, memory needs, context length, and any account or registry requirements. Check current release notes and recipe instructions because software versions and partner-system update timing can change.
- Follow the matching runtime instructions. Use NIM for a supported NIM image, vLLM for a model covered by its Spark recipe, or llama.cpp for a suitable GGUF checkpoint. Do not substitute a generic command for a model-specific recipe.
- Start the service and retain its data. Run the container or server using the current playbook, preserving model and cache directories where it recommends. Keep the endpoint on a trusted network unless you have put appropriate access controls in place.
- Check readiness and make a small test request. Wait for model loading, inspect logs and health status, then send a test request to the local endpoint documented by the chosen playbook. The NIM guide validates an OpenAI-compatible endpoint; use the endpoint and request format for your selected server.
If a model does not load
- Memory pressure or allocation failure: Reduce model or context requirements, use a smaller or quantized supported checkpoint, or stop unnecessary memory-heavy jobs. Quantization changes resource use and may affect output quality; the result depends on the model and configuration.
- Image or model cannot be found: Recheck the model-specific Spark image/profile, container tag, checkpoint format, and any registry or account prerequisites in the relevant NVIDIA guide.
- Service starts but requests fail: Check loading and health logs, then verify the endpoint and request format specified by the runtime’s playbook.
- Recipe calls for multiple systems: Do not assume a single Spark can run that distributed configuration. Prepare the additional system and network as that recipe specifies.
When a two-Spark setup is required
NVIDIA’s NIM deployment guide for DGX Spark covers selected large models using two Spark systems, ConnectX-7, verified 100 Gbps QSFP28 cables, and RoCE configuration. Its container workflow also calls for freeing memory on both machines and uses host networking and device mappings. These requirements belong to the models and procedure in that guide; they are not prerequisites for every LLM on Spark.
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Check software versions before deployment
NVIDIA’s Founders Edition release notes list DGX OS 7.5.0, GPU driver 580.159.03, and CUDA Toolkit 13.0.2 among the surfaced component versions. These are volatile release-note values, not evergreen requirements; NVIDIA says GB10-based partner systems may receive updates at different times. Check the live DGX Spark release notes and the chosen model recipe before deployment.
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