NVIDIA RTX Spark vs DGX Spark: Consumer AI PC vs Developer Workstation (2026)
NVIDIA now sells two “Spark” products and the naming is confusing. RTX Spark is a consumer Windows PC for personal AI agents. DGX Spark is a developer workstation running Linux for always-on AI development. Both have 128GB unified memory and Blackwell GPUs. They target completely different users.
Here is how to tell them apart and which one you actually need.
Quick comparison
| RTX Spark | DGX Spark | |
|---|---|---|
| Target user | Consumer, prosumer, creator | AI developer, researcher |
| OS | Windows (ARM) | Linux (Ubuntu) |
| Memory | 128GB unified | 128GB unified |
| GPU | Blackwell (consumer-grade) | Blackwell (data-center-class) |
| CPU | ARM (NVIDIA custom) | ARM (Grace) |
| Form factor | Laptops + compact desktops | Deskside workstation |
| Battery | All-day (laptops) | N/A (always plugged in) |
| CUDA | ✅ | ✅ |
| vLLM | ✅ (via WSL2) | ✅ (native Linux) |
| Docker/containers | Via WSL2 | ✅ Native |
| NemoClaw agents | ✅ (Windows + WSL) | ✅ (native, streamlined) |
| Always-on server | Not designed for it | ✅ Designed for 24/7 |
| Gaming/creative | ✅ | ❌ |
| Price (est.) | $2,000-4,000 | $3,000-5,000 |
| Available | Fall 2026 | Available now |
| OEM partners | ASUS, Dell, HP, Lenovo, MSI | NVIDIA direct |
When to choose RTX Spark
RTX Spark is your machine if you:
- Want one computer for everything — AI inference, gaming, creative work, daily tasks, and coding all on one device
- Need a laptop — RTX Spark comes in slim laptop form factor with all-day battery
- Use Windows — Native Windows apps, Office, Adobe, games
- Run AI models part-time — Spin up Ollama when needed, close it when gaming
- Want the simplest setup — Windows with LM Studio or Ollama, no Linux knowledge required
- Are a creator — Adobe Premiere/Photoshop optimized for RTX Spark, plus AI generation
When to choose DGX Spark
DGX Spark is your machine if you:
- Need always-on AI inference — Running local model servers 24/7 for development or production
- Work in Linux — Native Docker, vLLM, CUDA toolkit, no WSL layer
- Do serious ML development — Training, fine-tuning, research workflows that expect Linux
- Run production services locally — API servers, agent pipelines, continuous inference
- Need the full NVIDIA AI stack — NemoClaw, Nemotron, TensorRT without Windows compatibility layers
- Want a headless server — SSH in from any machine, runs unattended
Performance differences
Both have 128GB unified memory, but DGX Spark’s GPU is data-center-class Blackwell:
| Workload | RTX Spark | DGX Spark |
|---|---|---|
| LLM inference (27B Q4) | ~40-60 t/s | ~50-70 t/s |
| LLM inference (70B Q4) | ~12-20 t/s | ~15-25 t/s |
| Fine-tuning (14B LoRA) | Possible | Optimized |
| Multi-model serving | Limited | Better (native vLLM) |
| Sustained 24/7 load | Thermal concerns (laptop) | Designed for it |
The gap is not huge for inference — maybe 20-30% faster on DGX Spark. The real difference is the operating environment (Linux native vs Windows) and reliability under sustained load.
Software ecosystem comparison
| Tool | RTX Spark (Windows) | DGX Spark (Linux) |
|---|---|---|
| Ollama | ✅ Native Windows | ✅ Native Linux |
| LM Studio | ✅ | ✅ |
| llama.cpp | ✅ (NVIDIA-optimized) | ✅ (NVIDIA-optimized) |
| vLLM | Via WSL2 | ✅ Native |
| Docker | Via WSL2 | ✅ Native |
| OpenCode | ✅ | ✅ |
| NemoClaw | ✅ (Windows + WSL) | ✅ (streamlined installer) |
| TensorRT | ✅ | ✅ |
| PyTorch (CUDA) | ✅ | ✅ |
| Hermes Agent | ✅ (OpenShell) | ✅ |
| Adobe apps | ✅ Optimized | ❌ |
| Gaming | ✅ | ❌ |
The hybrid approach
Many developers will want both:
- DGX Spark as an always-on inference server under the desk (SSH in from anywhere)
- RTX Spark laptop for portable development and inference on the go
This gives you a local AI server running 24/7 (serving models to your whole network) plus a capable laptop for travel. Combined cost: ~$5,000-9,000 — comparable to a single high-end Mac Studio setup.
Also consider: DGX Station for Windows
NVIDIA also announced DGX Station for Windows — the ultimate AI desktop:
- Data-center-class GPU and CPU
- Windows for manageability
- Designed for professionals who need maximum local compute
- Price: enterprise (likely $10,000+)
This sits above both Spark products for users who need the absolute maximum local AI power on Windows.
FAQ
Can I run the same models on both?
Yes. Both have 128GB unified memory. Any model that fits on RTX Spark fits on DGX Spark and vice versa. See our best LLMs for RTX Spark guide for what fits in 128GB.
Is DGX Spark much faster than RTX Spark?
For inference: maybe 20-30% faster due to data-center-class GPU binning and better thermals. For most users, this difference is not worth the price premium and Linux requirement. The bigger difference is operational — DGX Spark is designed for 24/7 workloads.
Can I use RTX Spark as a server?
Technically yes (run vLLM via WSL2, expose the port). But it is not designed for sustained server workloads — thermal throttling on laptops, Windows updates interrupting service, etc. DGX Spark is the server option.
Which is better for fine-tuning?
DGX Spark. Fine-tuning benefits from native Linux, direct CUDA access without WSL overhead, and sustained thermal performance. RTX Spark can fine-tune smaller models but is not optimized for it.
I just want to run Ollama locally — which one?
RTX Spark. It is simpler (Windows, download Ollama, done), cheaper, and comes in laptop form. DGX Spark is overkill if you just want to chat with local models.
When does each ship?
DGX Spark is available now. RTX Spark ships fall 2026 from ASUS, Dell, HP, Lenovo, Microsoft Surface, and MSI.