# DGX Spark: Hello World > My first experiments with NVIDIA's DGX Spark, including local models with Ollama, LibreChat, and ComfyUI. - Published: 2025-12-31 - Author: Sven Scharmentke - Canonical: https://svnscha.de/posts/dgx-spark-hello-word/ - Tags: ai, nvidia, dgx-spark, ollama, librechat, comfyui, self-hosted, llm, stable-diffusion --- ## What Better Way to End 2025? So here's how I'm closing out the year: playing with an NVIDIA DGX Spark. Yes, *that* DGX Spark - the "personal AI supercomputer" that makes my wallet cry but my inner tech nerd do a happy dance. I have already spent some time testing LibreChat and ComfyUI on it. The courses at [stable-diffusion-art.com](https://stable-diffusion-art.com/) also helped me get started with image-generation workflows. And what better way to ring in the new year than with 128GB of unified memory and a Blackwell GPU sitting on my desk? I mean, some people do fireworks. I do neural networks. *(Yes, I'm aware that's possibly the nerdiest New Year's joke ever.)* Let me walk you through my first-day adventures getting this beautiful beast up and running. Fair warning: there may have been moments of childlike excitement, some creative problem-solving (read: frantically searching for a USB-C keyboard), and an unhealthy amount of "let's try this and see what happens." ## The Unboxing There's something special about unboxing new hardware. But unboxing a *DGX*? That hits different. The packaging screams premium - NVIDIA clearly understands that when you're dropping serious money on a personal AI supercomputer, presentation matters. Opening the box felt like a tech ritual. Inside, nestled in protective foam, sat this compact powerhouse that somehow packs a Blackwell GPU and 128GB of unified memory into a form factor that actually fits on a desk.
DGX Spark unboxing - first look DGX Spark - the unit DGX Spark - ready to go DGX Spark - booting
The unit itself is surprisingly compact. I expected something massive and loud, but NVIDIA engineered this thing to be almost civilized. Almost. We'll see how that holds up once I start pushing it with larger models. Everything was included - power cables, documentation, and that unmistakable feeling of *"I'm about to have way too much fun with this."* Alright, enough admiring the hardware. Time to make it do things. ## The Setup Experience I went with the local installation method - straightforward enough. Well, mostly. Here's a fun discovery: do you own a keyboard with USB Type-C? No? Neither did I. Fortunately, a laptop docking station saved the day. Crisis averted. Once the initial setup was complete, first things first - let's give this machine a proper identity: ```bash sudo hostnamectl set-hostname spark-1 sudo reboot ``` Why `spark-1`? Because who knows if there'll be a `spark-2` someday. Better to be prepared. Future me will thank present me for this foresight. Or curse me for the temptation. ## Installing Ollama After updating the system, I started with something familiar. [Ollama](https://ollama.com/) has become my go-to for local LLM deployment - it's absurdly simple to set up: ```bash curl -fsSL https://ollama.com/install.sh | sh ``` Now, I know what you're thinking - *"But what about vLLM? What about llama.cpp? What about those fancy self-compiled versions optimized for Blackwell's SM120 architecture?"* Yes, I've seen those posts. Yes, I'll explore them. But that's a future adventure. For now, let's start with the classics and make sure everything actually works before we go down the optimization rabbit hole. With Ollama ready, time to pull a model: ```bash ollama pull gpt-oss:20b ``` Could I run the massive 120B parameter models? Absolutely - that's kind of the whole point of having this hardware. But let's walk before we run. Download a smaller model to verify everything works, then let the big ones download overnight while I sleep. Strategy. ## Setting Up LibreChat Running models from the CLI is fine for testing, but I wanted a web interface. I previously used [Open WebUI](/posts/self-hosted-llm/) for my self-hosted LLM setup; this time I chose [LibreChat](https://github.com/danny-avila/LibreChat), an open-source chat UI that supports local models. Docker was pre-installed on the DGX Spark (nice touch, NVIDIA). Though my user wasn't in the docker group - but I'll assume you can figure that one out. ```bash mkdir -p ~/workspaces cd ~/workspaces/ git clone https://github.com/danny-avila/LibreChat.git cd LibreChat/ cp .env.example .env ``` After some experimentation, I settled on these configuration changes: ### Environment Configuration In `.env`, the key change: ```bash ENDPOINTS=custom ``` ### Docker Compose Override Create a `docker-compose.override.yml` for your custom settings, and a `librechat.yaml` for the LibreChat-specific configuration. **docker-compose.override.yml** ```yml services: api: volumes: - type: bind source: ./librechat.yaml target: /app/librechat.yaml ``` **librechat.yaml** ```yml --- version: 1.2.8 cache: true endpoints: custom: - name: "Ollama" apiKey: "ollama" baseURL: "http://host.docker.internal:11434/v1/" models: default: [ "gpt-oss:20b" ] fetch: true titleConvo: true titleModel: "current_model" summarize: false summaryModel: "current_model" forcePrompt: false modelDisplayLabel: "Ollama" ``` And that's... almost it. ## The Reverse Proxy Dance Here's where it gets slightly more involved. LibreChat enforces secure cookies by default - great for security, annoying when you're hitting F5 every five seconds during development and don't want to re-authenticate each time. The solution? Set up a reverse proxy with HTTPS. I use [Caddy](https://caddyserver.com/) in my home infrastructure because life's too short to manage SSL certificates manually. ```caddy chat.int.domain.tld { @lan remote_ip 172.16.29.0/24 handle { reverse_proxy @lan http://172.16.2.123:3080 } handle { templates respond "Access denied, {{.RemoteIP}}" 403 } } ``` This configuration does a few things: - Only allows access from my local network (the `172.16.29.0/24` range) - Proxies requests to the DGX Spark running LibreChat on port 3080 - Returns a 403 for anyone trying to access from outside It's not always open from outside but when it is (to refresh certificates) this is a great and easy way to deal with this. I've also configured a custom DNS zone in my router to make `chat.int.domain.tld` resolve correctly, but that's infrastructure stuff for another post. ## First Impressions And just like that - we're live. Quick setup, everything working, ready to chat with local models running on actual Blackwell hardware. The response times are snappy, the interface is clean, and I can already tell this is going to be a fun playground. ## Next Steps Now that the basics are running, it's time to explore what this hardware can actually do. On my list: - **Document Understanding**: Feed it files and see how well it comprehends them - **Voice integration**: Testing spoken conversations with local models - **Image generation with ComfyUI**: Exploring image-generation workflows on the Blackwell GPU. The [stable-diffusion-art.com](https://stable-diffusion-art.com/) courses have been a useful starting point. These are just the fundamentals to get comfortable with the platform. But honestly? Between LibreChat's slick interface for chatting with local models and ComfyUI's node-based wizardry for image generation, I've already had more fun than should be legal. It was a good way to end 2025: experimenting with new hardware and learning what it can do. Here's to closing out the year with new adventures, and to many more in 2026! 🎉 I will share more results as I continue testing the DGX Spark.