DGX Spark Expert

Expert guidance for NVIDIA DGX Spark AI workstation development.

Quick Reference

Resource	URL
Playbooks Hub	https://build.nvidia.com/spark
User Guide	https://docs.nvidia.com/dgx/dgx-spark/
Support	https://www.nvidia.com/en-us/support/dgx-spark/
Forums	https://forums.developer.nvidia.com/c/accelerated-computing/dgx-spark-gb10
GitHub Playbooks	https://github.com/NVIDIA/dgx-spark-playbooks

Key System Facts

• Architecture: ARM64 (not x86) — use ARM64 binaries/containers
• Memory: 128GB unified (shared CPU/GPU via UMA)
• Performance: 1 PFLOP FP4 with sparsity
• Max Model Size: ~200B parameters (single), ~405B (two Sparks stacked)
• OS: DGX OS (Ubuntu-based with NVIDIA stack)

Reference Files

Load these for detailed information:

• references/hardware-specs.md — Full specs, UMA details, Spark stacking
• references/playbooks-index.md — All 25+ official playbooks with links
• references/known-issues.md — Troubleshooting, diagnostics, support
• references/software-stack.md — DGX OS, containers, frameworks, tools
• references/ai-workbench.md — AI Workbench projects, RAG, agents

Common Workflows

Run Inference

Quick local chat: Use Ollama + Open WebUI playbook

# Install Ollama
curl -fsSL https://ollama.com/install.sh | sh
ollama run llama3.2

Production serving: Use vLLM or TRT-LLM playbooks

# vLLM example
pip install vllm
python -m vllm.entrypoints.openai.api_server \
  --model meta-llama/Llama-3.1-8B-Instruct

Fine-tune a Model

1. Quick experiments: LLaMA Factory or Unsloth playbooks
2. Production: NeMo playbook
3. Image models: FLUX Dreambooth playbook

See references/playbooks-index.md for all options.

Create AI Workbench Project

# Clone official RAG project
nvwb project clone https://github.com/NVIDIA/workbench-example-agentic-rag

# Start project
cd workbench-example-agentic-rag
nvwb start

See references/ai-workbench.md for detailed workflow.

Connect Two Sparks

1. Connect via QSFP/CX7 cable
2. Configure netplan on both nodes
3. Exchange SSH keys
4. Install NCCL and run tests
5. See "Connect Two Sparks" and "NCCL" playbooks

Troubleshoot Issues

1. Check references/known-issues.md first
2. Common fixes:
   • Memory issues: sudo sync && echo 3 | sudo tee /proc/sys/vm/drop_caches
   • Check memory: free -h (not nvidia-smi for memory)
   • Driver issues: sudo systemctl status nvidia-persistenced
3. Forums: https://forums.developer.nvidia.com/c/accelerated-computing/dgx-spark-gb10

UMA Memory Management

DGX Spark uses Unified Memory Architecture — CPU and GPU share 128GB.

Key points:

• nvidia-smi memory display may show "Not Supported" (expected)
• Use free -h for actual memory status
• Flush buffer cache if memory pressure: sudo sync && echo 3 | sudo tee /proc/sys/vm/drop_caches
• Models up to ~200B parameters can run locally

Container Patterns

# Standard GPU container
docker run --gpus all --runtime nvidia <image>

# With shared memory (required for many ML frameworks)
docker run --gpus all --shm-size=16g <image>

# Mount HuggingFace cache
docker run --gpus all \
  -v $HOME/.cache/huggingface:/root/.cache/huggingface \
  <image>

# NGC container example
docker pull nvcr.io/nvidia/pytorch:24.01-py3

ARM64 Compatibility

DGX Spark runs ARM64, not x86. When installing software:

• Use aarch64 or arm64 package versions
• NGC CLI must be ARM64 Linux version
• Some x86-only tools require alternatives or won't work
• Check NGC for ARM64-compatible containers

Playbook Selection Guide

Goal	Recommended Playbook
Chat with local LLM	Open WebUI + Ollama
Serve LLM API	vLLM or TRT-LLM
Fine-tune LLM	LLaMA Factory (quick) or NeMo (production)
Build RAG app	RAG in AI Workbench
Generate images	Comfy UI
AI coding assistant	Vibe Coding
Remote access	Tailscale
Data science	CUDA-X Data Science
Large models (>200B)	Connect Two Sparks

Decision Logic

User asks about inference → Check model size, recommend vLLM (high throughput) or TRT-LLM (optimized latency), or Ollama for simple use.

User asks about fine-tuning → Assess complexity: LLaMA Factory/Unsloth for experiments, NeMo for production, PyTorch for custom needs.

User asks about AI Workbench → Load references/ai-workbench.md, guide through project creation/cloning.

User reports error/issue → Load references/known-issues.md, check if known issue, provide diagnostic commands.

User asks about specs/capabilities → Load references/hardware-specs.md, provide relevant details.

User wants specific playbook → Load references/playbooks-index.md, provide direct link and summary.