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To run an LLM locally on NVIDIA DGX Spark, choose a runtime that matches the model format and your goal: use llama.cpp for flexible GGUF experimentation and a local API, vLLM for NVIDIA’s hardware-specific serving recipes, or LM Studio’s llmster for a headless local service and client access. Then check the exact model’s memory and disk requirements before downloading it. DGX Spark has 128 GB of unified system memory, but NVIDIA’s claim that it supports models of up to 200 billion parameters is not a guarantee that every model, quantization, or context length will fit or run well.
Choose a runtime based on what you want to do
These are workflow options, not a measured performance ranking. NVIDIA’s reviewed guides do not establish an independent head-to-head benchmark or a universally best model for DGX Spark.
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| Your priority | Workflow | What it offers | Check before choosing |
|---|---|---|---|
| Experiment with GGUF models and expose a local API | llama.cpp | CUDA-enabled build, GGUF loading, and llama-server with an OpenAI-compatible API. |
Model quantization, context and KV-cache demand, available memory, download size, and build requirements. |
| Serve a configuration with a hardware-specific launch recipe | vLLM | NVIDIA provides recipes matched to Spark configurations, including a recommended single-Spark recipe. | Exact model variant, recipe, container, precision, parser, parallelism, and memory headroom. |
| Run a headless local service and connect from a client | LM Studio / llmster | A terminal-native service and local API, with an LM Studio SDK workflow for a laptop client. | Prerequisite memory and storage, selected model footprint, and the API or client you plan to use. |
For a broader overview of local AI runtimes, see NVIDIA’s local AI overview.
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DGX Spark has 128 GB of unified system memory shared across its CPU/GPU system architecture, according to the DGX Spark hardware overview. NVIDIA describes support for models up to 200 billion parameters on one system; this is a capacity statement, not a guarantee that any arbitrary model configuration will fit or perform acceptably. NVIDIA’s launch announcement also described up to 70 billion parameters for local fine-tuning and up to 200 billion for inference; those are vendor claims, not independent benchmark results (NVIDIA Newsroom).
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Parameter count alone does not determine whether a model fits. The weights, runtime overhead, and KV cache must coexist in available memory. The KV cache grows with the context held during generation, so a configuration that loads at a short context may need more memory at a longer one. Quantization and model format also affect the footprint. In addition to memory, allow for the model download and runtime build files on disk.
- Use the requirements for the exact model and recipe, rather than treating “up to 200B” as a sizing shortcut.
- Check how much memory remains available to the runtime after system use and account for the context length you intend to use.
- Check free disk space before downloading; a model’s file size is separate from the memory needed to run it.
Check your DGX Spark software version
Before copying setup commands, identify whether your system is a Founders Edition or a GB10-based partner system, then check its DGX OS, driver, and CUDA versions. NVIDIA’s release notes list DGX OS 7.5.0, NVIDIA GPU driver 580.159.03, and CUDA Toolkit 13.0.2 for the Founders Edition release covered there. Partner systems may receive updates on a different schedule, so consult the DGX Spark release notes for your specific system before following version-sensitive instructions.
Run GGUF models with llama.cpp
Choose llama.cpp when you want to work with GGUF checkpoints and a straightforward local inference endpoint. NVIDIA’s walkthrough builds llama.cpp from source with CUDA so it can use the GB10 GPU, downloads a GGUF model, and starts llama-server. The server provides an OpenAI-compatible /v1/chat/completions endpoint.
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- Review the current walkthrough’s prerequisites and commands for your installed software stack.
- Build llama.cpp with CUDA as directed, so the runtime can use the Spark GPU.
- Download a GGUF checkpoint only after checking its size, quantization, and memory needs against your intended context length.
- Start
llama-serverusing the walkthrough’s command and connect a compatible client to its OpenAI-style chat-completions endpoint.
Serve a hardware-matched model with vLLM
Choose vLLM when you want a serving workflow based on a recipe for your Spark configuration. NVIDIA’s vLLM recipe guide recommends Qwen3.8-27B NVFP4 for one DGX Spark. The guide says this quantized model fits one Spark and provides a hardware-specific configuration with reasoning and tool-calling capabilities.
For another model, select a recipe for the exact hardware, model variant, precision, and capabilities you need, then follow its complete container, environment, and serving command. An alternate recipe may require different model downloads, containers, memory assumptions, parser settings, or parallelism. Do not assume that a command or resource requirement from one recipe transfers unchanged to another.
Run a headless local API with LM Studio
LM Studio’s DGX Spark workflow uses llmster, a headless, terminal-native service, to run inference locally through an API. NVIDIA’s playbook also describes connecting to the service from a laptop through the LM Studio SDK. Example models listed in the playbook include Nemotron 3 Nano Omni, Qwen3.6-35B-A3B, and GPT-OSS-120B; these examples are not a guarantee that every model setting or context length will fit.
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The LM Studio playbook, updated April 28, 2026, specifies an ARM64 and Blackwell DGX Spark, at least 65 GB of memory and storage, and recommends 70 GB or more. These are workflow prerequisites; check the selected model’s actual footprint as well. The playbook describes optional LM Link for remote client access as end-to-end encrypted. Check current product terms and configuration details if you plan to use it.
Quick Recap
Make the model choice fit the workflow
- Decide how you will use the model. Choose GGUF experimentation and a simple local endpoint for llama.cpp, recipe-based serving for vLLM, or a headless local service with client access for llmster.
- Match the model to the runtime. Confirm the format and precision supported by the selected workflow; GGUF and NVFP4 are not interchangeable labels.
- Check practical fit. Account for weights, runtime overhead, KV cache at your intended context length, and memory already in use.
- Check disk and prerequisites. Include the download and, where relevant, build artifacts; confirm software versions and recipe-specific containers or settings.
- Follow the current official instructions. Model names, recipes, and software stacks can change. Use the NVIDIA walkthrough for the runtime you selected rather than mixing commands from different workflows.
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