[Day 1] DGX Spark Came Home — I Made It Draw a Cat

PEPPERCORN — Mon, 04 May 2026 03:20:48 +0000

[Day 1] DGX Spark Came Home — I Made It Draw a Cat

So... what is "local LLM" again?

Honestly, I'm still figuring out what "local LLM" even means. But somehow, through a series of decisions I won't fully justify here, I ended up buying an NVIDIA DGX Spark — and now it's sitting in my house.

DGX Spark: NVIDIA's "supercomputer for the home" — a small but seriously expensive box with the latest-gen AI chip inside. Apparently.

What I really want to figure out is: when should I use local AI vs. cloud AI? Reading articles about it doesn't seem to help, so I'm going full hands-on. Goal: 100 experiments, one per day-ish, until I have an evidence-based answer.

This is experiment#1.

First, the hardware

So this is what showed up at my door — solidly packed in a sturdy cardboard box.

When I opened it, I was surprised at how small it actually is. "This is the AI machine?" kind of small.

Boot up → Initial OS setup

Power on, and an Ubuntu-based DGX OS 7.5.0 boots up.

Welcome screen

"Get started" — yes, please.

Language and timezone

Standard Linux installer territory — same as Ubuntu?

Privacy settings

Diagnostic data sharing prompts.

System update

The moment I plugged it in, it started updating itself. Modern Linux being Linux.

Setup complete

I picked a username and let the hostname auto-assign. DGX-side prep done.

Connecting from my Windows PC

Plugging a monitor into the DGX every time would be tedious, so I want to SSH in from my regular Windows machine (which I've nicknamed "myPC1").

NVIDIA provides a desktop app called NVIDIA Sync that's supposed to make SSH setup painless. So I install it.

…and that's where I fell into a trap big-time. Windows OpenSSH refused to connect with a "your SSH config has weird permissions, can't trust it" error.

Full troubleshooting steps are in the collapsible "Tech details" section below.

Inside the DGX, finally

After much wrestling, I made it inside. Here's the rough lay of the land:

Item	Spec
GPU	NVIDIA GB10 Grace Blackwell
Memory	128GB (unified between CPU and GPU)
Storage	4TB SSD (basically empty)
CPU	20 cores (perf + efficiency combo)
Idle power	4W (yes, four)

128GB of memory is apparently 8–16x what's in a typical laptop.

Setting up image generation → 🐱

This is the main event. I'm setting up ComfyUI to generate the first cat from this DGX.

The ComfyUI interface looks like this:

The "boxes connected by cables" view is intimidating at first, but the default workflow is pre-wired. You just type a prompt and hit Queue Prompt.

So:

a cute fluffy cat sitting on a sunny windowsill, photorealistic, high detail, beautiful lighting, soft fur, cinematic, masterpiece, best quality

A few seconds later...

🐱 There it is — the very first cat my DGX has ever drawn!

Tweaked the prompt and made some more.

Eyes a bit unsettling but yeah, fluffy cat.

Going a touch dark there.

…is this a cat? It feels artistic though.

Distinctive composition.

Each masterpiece takes a few to a dozen seconds. That speed means I can iterate on prompts without thinking about cost — which turned out to be quite addictive.

Tech details (let the AI explain it)

The rest is the technical stuff. Read on if you're curious.

I'm a non-engineer poking at this stuff for the first time, so I had Claude (my AI pair programmer for this challenge) write up the technical details. Hopefully useful for anyone walking the same path.

How to actually get SSH working on Windows

NVIDIA Sync should generate an SSH keypair, register the public key on the DGX side at ~/.ssh/authorized_keys, and let you connect without a password.

If it doesn't work, the cause is usually permissions on Windows SSH config files.

Symptom

$ ssh spark-XXXX.local
Bad permissions. Try removing permissions for user: [PC]\CodexSandboxUsers
on file C:/Users/[user]/.ssh/config.

If you've installed Codex CLI or similar sandboxing tools in the past, the [PC]\CodexSandboxUsers group may have inherited permissions on ~/.ssh/.

Fix (run from an elevated PowerShell)

Use environment variables to avoid hard-coding your username/PC name.

# Take ownership
takeown /f "$env:USERPROFILE\.ssh\config"
icacls "$env:USERPROFILE\.ssh\config" /grant:r "$($env:USERNAME):F"

# Disable inheritance and remove the bad user
icacls "$env:USERPROFILE\.ssh\config" /inheritance:d
icacls "$env:USERPROFILE\.ssh\config" /remove "$env:COMPUTERNAME\CodexSandboxUsers"

Use /inheritance:d rather than /inheritance:r — :r strips all permissions, locking yourself out.

NVIDIA Sync's internal config files need the same treatment

~/.ssh/config Includes an NVIDIA Sync config file, and that one inherits the same problem.

$cfg = "$env:LOCALAPPDATA\NVIDIA Corporation\Sync\config\ssh_config"
icacls $cfg /inheritance:d
icacls $cfg /remove "$env:COMPUTERNAME\CodexSandboxUsers"

$key = "$env:LOCALAPPDATA\NVIDIA Corporation\Sync\config\nvsync.key"
icacls $key /inheritance:d
icacls $key /remove "$env:COMPUTERNAME\CodexSandboxUsers"

Ghost SIDs that icacls can't remove

If you have SIDs from deleted user accounts lingering, icacls /remove won't touch them. You need PowerShell ACL manipulation:

$cfg = "$env:LOCALAPPDATA\NVIDIA Corporation\Sync\config\ssh_config"
$acl = Get-Acl $cfg
$badRules = $acl.Access | Where-Object {
    $_.IdentityReference.Value -like "S-1-5-*" -and
    $_.IdentityReference.Translate([System.Security.Principal.NTAccount]) -isnot [System.Security.Principal.NTAccount]
}
$badRules | ForEach-Object { $acl.RemoveAccessRule($_) | Out-Null }
Set-Acl -Path $cfg -AclObject $acl

After this, ssh spark-XXXX.local connects on the first try (replace XXXX with your hostname).

Commands to check DGX specs

# GPU
$ nvidia-smi
NVIDIA-SMI 580.142    Driver Version: 580.142    CUDA Version: 13.0
GPU 0: NVIDIA GB10    36C    P8    4W / N/A

# OS
$ uname -a
Linux spark-XXXX 6.17.0-1014-nvidia ... aarch64 GNU/Linux

# Memory
$ free -h
Mem: 121Gi  2.6Gi  118Gi

# Storage
$ df -h
/dev/nvme0n1p2  3.7T  47G  3.5T  2%  /

# CPU
$ lscpu
Architecture:  aarch64
CPU(s):        20
Model name:    Cortex-X925 + Cortex-A725

Notable bits:

CUDA 13.0 (latest)
aarch64 (ARM64) architecture — yes, the DGX is ARM
121Gi (≈128GB) unified memory
20 cores in big.LITTLE layout (10 perf + 10 efficient)

ComfyUI installation steps

Following the official NVIDIA Comfy UI playbook.

# Virtual environment
cd ~
python3 -m venv comfyui-env
source comfyui-env/bin/activate

# PyTorch with CUDA 13.0
pip3 install torch torchvision --index-url https://download.pytorch.org/whl/cu130

# ComfyUI itself
git clone https://github.com/comfyanonymous/ComfyUI.git
cd ComfyUI
pip install -r requirements.txt

# Model (SD 1.5, ~2GB)
cd models/checkpoints/
wget https://huggingface.co/Comfy-Org/stable-diffusion-v1-5-archive/resolve/main/v1-5-pruned-emaonly-fp16.safetensors

# Launch server
cd ~/ComfyUI
python main.py --listen 0.0.0.0

Key packages installed:

torch 2.11.0+cu130
cuDNN 9.19
NCCL 2.28
transformers 5.7.0
comfyui-frontend-package 1.42.15

Open http://spark-XXXX.local:8188 from your Windows PC's browser to access ComfyUI (XXXX is your hostname).

Download speed

The 2GB model came down at 40.6 MB/s in 50 seconds from HuggingFace's CDN. About half of my home 1Gbps LAN.

Tomorrow: Day 2

Day 2 plan: Train a LoRA on photos of my actual cat.

Today's SD 1.5 only knows "some cat from somewhere". With LoRA fine-tuning, I should be able to teach it about my specific cat. That kind of personalization feels like the killer feature of running locally.

100ExperimentsWithDGX #LocalLLM

DEV Community: PEPPERCORN

[Day 1] DGX Spark Came Home — I Made It Draw a Cat

[Day 1] DGX Spark Came Home — I Made It Draw a Cat

So... what is "local LLM" again?

First, the hardware

Boot up → Initial OS setup

Welcome screen

Language and timezone

Privacy settings

System update

Setup complete

Connecting from my Windows PC

Inside the DGX, finally

Setting up image generation → 🐱

Tech details (let the AI explain it)

Symptom

Fix (run from an elevated PowerShell)

NVIDIA Sync's internal config files need the same treatment

Ghost SIDs that icacls can't remove

Download speed

Tomorrow: Day 2

100ExperimentsWithDGX #LocalLLM