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onboarding

  • Set Up Local Network Access
  • Open WebUI with Ollama

data science

  • Single-cell RNA Sequencing
  • Portfolio Optimization
  • CUDA-X Data Science
  • Text to Knowledge Graph
  • Optimized JAX

tools

  • DGX Dashboard
  • Comfy UI
  • Connect Three DGX Spark in a Ring Topology
  • Connect Multiple DGX Spark through a Switch
  • RAG Application in AI Workbench
  • Set up Tailscale on Your Spark
  • VS Code

fine tuning

  • FLUX.1 Dreambooth LoRA Fine-tuning
  • LLaMA Factory
  • Fine-tune with NeMo
  • Fine-tune with Pytorch
  • Unsloth on DGX Spark

use case

  • NemoClaw with Nemotron 3 Super and Telegram on DGX Spark
  • Secure Long Running AI Agents with OpenShell on DGX Spark
  • OpenClaw 🦞
  • Live VLM WebUI
  • Install and Use Isaac Sim and Isaac Lab
  • Vibe Coding in VS Code
  • Build and Deploy a Multi-Agent Chatbot
  • Connect Two Sparks
  • NCCL for Two Sparks
  • Build a Video Search and Summarization (VSS) Agent
  • Spark & Reachy Photo Booth

inference

  • Run models with llama.cpp on DGX Spark
  • vLLM for Inference
  • Nemotron-3-Nano with llama.cpp
  • Speculative Decoding
  • SGLang for Inference
  • TRT LLM for Inference
  • NVFP4 Quantization
  • Multi-modal Inference
  • NIM on Spark
  • LM Studio on DGX Spark

LM Studio on DGX Spark

30 MIN

Deploy LM Studio and serve LLMs on a Spark device; use LM Link to access models remotely.

InferenceLM LinkLM Studiollmster
View on GitHub
OverviewOverviewInstructionsInstructionsTroubleshootingTroubleshooting
SymptomCauseFix
API returns "model not found" errorModel not downloaded or loaded in LM StudioRun lms ls to verify download status, then load model with lms load {model-name}
lms command not foundPATH issue assuming successful installationRefresh your shell by running source ~/.bashrc
Model load fails - CUDA out of memoryModel too large for available VRAMSwitch to a smaller model or a different quantization
LM Link: devices not connecting or remote models not visibleDevices not in same Link, or LM Link not set up on bothEnsure both Spark and laptop are signed in and joined to the same Link at lmstudio.ai/link. Restart LM Studio/llmster after joining. See LM Link for how it works.

NOTE

DGX Spark uses a Unified Memory Architecture (UMA), which enables dynamic memory sharing between the GPU and CPU. With many applications still updating to take advantage of UMA, you may encounter memory issues even when within the memory capacity of DGX Spark. If that happens, manually flush the buffer cache with:

sudo sh -c 'sync; echo 3 > /proc/sys/vm/drop_caches'

For the latest known issues, please review the DGX Spark User Guide.

Resources

  • LM Studio Documentation
  • LM Link (use local models remotely)
  • DGX Spark Documentation
  • DGX Spark Forum
  • LM Studio Discord
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