DGX Spark vs DIY 4×RTX 3090 Rig: Which AI Setup Wins?

1. Two Paths to Local AI — Same Budget, Very Different Machines

You've decided to run AI models locally. No more cloud bills, no more rate limits, no more sending sensitive data to someone else's servers. You have roughly $4,000 to spend, and in early 2026 two radically different paths sit at the same price point.

Path A is the NVIDIA DGX Spark ($3,999) — a sealed, Mac Mini-sized box powered by the Grace Blackwell GB10 chip with 128 GB of unified memory. Plug it in, power it on, run 200B-parameter models. One petaFLOP of AI compute in a silent golden cube.

Path B is a DIY 4×RTX 3090 rig (~$3,600) — an open-air mining frame stuffed with four used RTX 3090 GPUs, each with 24 GB of VRAM, totaling 96 GB of dedicated GPU memory. It's loud, it's ugly, and it draws 1,400 watts at full tilt. But it has raw CUDA horsepower that embarrasses the Spark on models that fit in VRAM.

This post puts them head-to-head across 13 categories with real benchmark data. By the end, you'll know exactly which one to buy — and it depends entirely on what you plan to run.

2. Side-by-Side Specs

3. Raw Performance — Tokens Per Second

This is the category everyone cares about most. We collected benchmark data from Ollama's official DGX Spark tests, community benchmarks on GitHub, vLLM 4×3090 experiments, and Reddit comparisons. All numbers are decode speed (token generation) unless noted.

Why is the Spark slower on smaller models? Its 128 GB of unified LPDDR5x memory has a bandwidth of ~273 GB/s. Each RTX 3090 has ~936 GB/s of GDDR6X bandwidth. LLM inference is memory-bandwidth-bound during token generation — the GPU that can read weights faster generates tokens faster. Four 3090s have 13.7× more aggregate memory bandwidth than the Spark.

John Carmack publicly noted that the DGX Spark appears to max out at ~100W of actual power draw — about half its 240W rating — delivering roughly half the advertised 1 PFLOP performance. NVIDIA has acknowledged this and pointed to firmware updates, but as of early 2026 the real-world FP4 throughput is closer to ~480 TFLOPS.

4. Memory & Maximum Model Size

DGX Spark: 128 GB Unified

The Spark's 128 GB is shared between CPU and GPU with no copy overhead. NVIDIA states ~120 GB is available for models after OS/framework overhead. This means you can load:

• 200B parameter models at FP4/MXFP4 quantization (~50 GB)
• 120B models at FP8 (~60 GB)
• 70B models at FP16 (~140 GB — doesn't fit; needs Q8 or lower)
• 405B models — too large even at aggressive quantization

DIY 4×3090: 96 GB Dedicated VRAM

Four RTX 3090s provide 96 GB of dedicated GDDR6X VRAM. With tensor parallelism across 4 GPUs:

• 70B parameter models at Q4 quantization (~40 GB) — fits easily, runs fast
• 70B at FP16 (~140 GB) — does not fit
• 120B at Q4 (~60 GB) — fits but tight; limited KV cache
• 200B+ models — cannot load

5. Energy Cost

Using $0.12/kWh US average. The DGX Spark sips power — roughly 10× less than the 4×3090 rig under load. Running the 3090 rig 24/7 adds $1,300-$1,600/year in electricity. That's nearly half the cost of the rig itself every year.

However: you can power-limit each 3090 to ~220W (from 350W) and lose only ~10% performance, dropping the rig to ~930W at load and saving ~30% on electricity. The vLLM benchmark data shows 220W is the sweet spot for efficiency.

6. Noise & Physical Footprint

DGX Spark

150×150×50 mm. Fits on your desk next to your monitor. Near-silent operation — early reviewers describe it as inaudible under normal workloads. TechRadar called it "gorgeous engineering."

DIY 4×3090

A Veddha 8-GPU open-air mining frame is roughly 60×35×35 cm — about the size of a small nightstand. It's an exposed skeleton of aluminum with four large graphics cards hanging off it. Under load, four 3090 fans spin up to ~2,000 RPM producing 45-55 dBA — comparable to a vacuum cleaner. It belongs in a closet, basement, or spare room — not on your desk.

7. Setup Complexity

DGX Spark: ~30 minutes

Unbox, plug in power and Ethernet, power on. Ubuntu is preinstalled with NVIDIA's AI stack (CUDA, cuDNN, TensorRT, container runtime). Install Ollama with one command. It's as close to plug-and-play as AI hardware gets.

DIY 4×3090: 1-3 days

Assemble the open-air frame. Mount the motherboard. Install CPU, RAM, SSD. Wire dual server PSUs through breakout boards. Mount 4 GPUs on risers. Install Ubuntu. Install NVIDIA drivers. Configure CUDA. Set up vLLM or Ollama with tensor parallelism. Debug PCIe riser issues. Power-limit GPUs. Configure fan curves. Set up Open WebUI for a chat interface. You'll learn a lot — but it's a weekend project minimum.

8. Upgradeability

DGX Spark: Sealed — No Upgrades

The GB10 is a system-on-chip. You cannot add RAM, swap the GPU, or expand storage (beyond external drives). What you buy is what you get. If 128 GB isn't enough in 2028, you buy a new unit.

DIY 4×3090: Fully Expandable

• Add 2 more 3090s to reach 6 GPUs (144 GB VRAM) — the H510 BTC+ supports 6 slots
• Swap in RTX 5090s or PRO 6000s when prices drop (though the budget board's PCIe 3.0 ×1 limits them — see our Budget vs Pro comparison)
• Upgrade to the Pro tier (ROMED8-2T) for PCIe 4.0 ×16 and up to 7-13 GPUs
• Add more RAM, faster storage, 10GbE networking

9. Software Compatibility

DGX Spark: ARM64 + Blackwell CUDA

• Ollama — Full support, officially benchmarked
• vLLM — Works, though ARM64 builds are newer and less battle-tested
• SGLang — Officially supported, LMSYS published DGX Spark benchmarks with speculative decoding (2× speedup)
• PyTorch/TensorFlow — Full support via NVIDIA containers
• Random GitHub repos — Many x86-only scripts/tools won't work without modification

DIY 4×3090: x86_64 + Ampere CUDA

• Everything works. The RTX 3090 is the most battle-tested AI GPU in existence
• vLLM, Ollama, llama.cpp, exllamaV2, TabbyAPI — all first-class support
• Every Docker container, every Python package, every CUDA kernel — built for x86 + Ampere first
• Compute capability 8.6 — supports FP16, BF16, INT8, but no native FP4 (that's Blackwell only)

10. Multi-User / Serving

DGX Spark

The Spark comes with a ConnectX-7 NIC (100GbE capable, though you need the right switch). It can serve multiple users via vLLM or SGLang, but the single-stream decode speed is relatively slow (~10-40 tok/s depending on model). Batched throughput is better, but the memory bandwidth ceiling limits concurrent users.

DIY 4×3090

vLLM on 4×3090 excels at multi-user serving. The vLLM benchmarks show 353-400 tok/s total throughput on QwQ-32B with batched requests. With continuous batching, you can serve 5-10+ concurrent users at acceptable speeds. The bottleneck is the 1 GbE network on the budget build — upgrade to 10 GbE with the Pro tier.

11. Training vs Inference

DGX Spark

NVIDIA's own benchmarks show the Spark can fine-tune a 3B model via LoRA at decent speeds. For full fine-tuning of larger models (7B+), the limited compute and memory bandwidth make it impractical. It's designed as an inference and development machine, not a training rig.

DIY 4×3090

Four RTX 3090s are a legitimate training platform. Each card has 24 GB of VRAM and 35.6 TFLOPS of FP16 compute. With DeepSpeed ZeRO or FSDP, you can fine-tune 7B-13B models across 4 GPUs. The PCIe 3.0 ×1 bandwidth is a real problem for training though — gradient synchronization is constantly limited by the ~1 GB/s inter-GPU link. The Pro tier build with PCIe 4.0 ×16 is strongly recommended for training.

12. Resale Value

RTX 3090: Proven resale market

The RTX 3090 has held remarkably steady at $650-$800 on eBay since 2024. It's a known quantity with a massive secondary market. If you decide AI isn't for you, or you upgrade to 5090s, you can sell your 3090s and recover 70-80% of your investment.

DGX Spark: Unknown resale

The DGX Spark is a proprietary, sealed ARM device. Its resale market is tiny and uncertain. It's unlikely to hold value as well as commodity GPUs, especially once the next generation (DGX Spark 2?) arrives. First-gen proprietary hardware historically depreciates faster than commodity components.

13. Total Cost of Ownership

Let's calculate the real cost of owning each system, assuming 8 hours/day of active inference at $0.12/kWh:

Surprising result: after 3 years with electricity and resale factored in, the budget 3090 rig is actually the cheapest option — because used 3090 GPUs hold their value so well. The Spark's electricity savings (~$1,300 over 3 years) are partially offset by its lower resale value.

If you run 24/7 instead of 8h/day, the math shifts dramatically toward the Spark — the 3090 rig's electricity cost balloons to $4,300+ over 3 years.

14. The Verdict

The ideal $4,300 setup? The Pro tier 4×3090 rig (ROMED8-2T + EPYC + PCIe 4.0 ×16). You get the 3090 rig's raw speed, proper PCIe bandwidth for training and future GPU upgrades, IPMI remote management, and 10GbE networking. It costs $300 more than the DGX Spark and outperforms it on every model up to 70B. Check our Budget vs Pro Tier comparison and Pro Tier shopping list for the full build guide.

This article was written collaboratively by Michel (human) and Yaneth (AI agent) as part of ThinkSmart.Life's research initiative. Prices reflect February 2026 market conditions and may fluctuate.