AORUS RTX 5090 AI BOX vs NVIDIA DGX Spark for Local AI

Two products, both promising to put serious AI compute on your desk, both priced under $5,000, and both solving fundamentally different problems. The Gigabyte AORUS RTX 5090 AI BOX is a 575W discrete GPU in an external enclosure with 32 GB of GDDR7. The NVIDIA DGX Spark is a complete mini PC built around the Grace Blackwell Superchip with 128 GB of unified memory. One is a peripheral. The other is a standalone computer. Comparing them requires understanding what you actually need.

The Core Tradeoff: Bandwidth vs Capacity

This comparison comes down to a single architectural difference that determines everything else.

The AORUS AI BOX has a discrete GPU with its own dedicated GDDR7 memory. That memory is fast - 1,792 GB/s - but limited to 32 GB. The DGX Spark has a unified memory architecture where CPU and GPU share 128 GB of LPDDR5x at 273 GB/s. Four times the capacity, one-sixth the bandwidth.

For AI inference, this tradeoff plays out predictably: the AI BOX runs smaller models much faster, while the DGX Spark can load models the AI BOX physically cannot fit.

Specifications Side by Side

The numbers tell the story immediately. The AI BOX has 3.5x more CUDA cores and 6.5x more memory bandwidth. The DGX Spark has 4x more memory capacity and the ability to cluster two units together for 256 GB total. They're optimized for completely different workloads.

Inference Performance

Small Models (8B-14B) - AI BOX Wins Decisively

For models that fit comfortably in 32 GB, the AI BOX's bandwidth advantage translates directly to faster inference. The desktop RTX 5090's benchmarks (subtract roughly 10-15% for the Thunderbolt 5 overhead on compute workloads) significantly outpace the DGX Spark at this model size.

The gap is large. The RTX 5090's 1,792 GB/s bandwidth means token generation is limited by compute rather than memory reads, while the DGX Spark's 273 GB/s becomes the bottleneck during decode.

For interactive chat with a single user, both are fast enough - 20 tokens per second versus 70 tokens per second is not a meaningful user experience difference. But for batch inference, API serving, or any throughput-sensitive application, the AI BOX is the clear winner at this model scale.

Large Models (70B+) - DGX Spark Wins by Default

This is where the comparison flips. A 70B parameter model at FP8 requires approximately 70 GB of memory. The AI BOX's 32 GB cannot hold it without aggressive quantization (Q4 or lower). The DGX Spark loads it into its 128 GB unified memory without compromise.

The DGX Spark's 2.7 tokens per second on 70B isn't fast - it's limited by that 273 GB/s bandwidth - but it works at full precision. The AI BOX can technically run 70B at Q4 quantization, trading model quality for fitting within 32 GB. Two DGX Sparks clustered via QSFP can even run 235B parameter models, a capability the AI BOX has no path to.

If your workflow requires loading models above 30B at reasonable precision, the DGX Spark is the only option between these two.

Fine-Tuning and Training

Both devices can handle LoRA fine-tuning, but they approach it differently.

The DGX Spark supports full CUDA with torch.compile, which is a significant advantage for training workflows. It runs NeMo, Unsloth, and LLaMA Factory with a pre-configured software stack. Fine-tuning Llama 3.1 8B with LoRA on Unsloth hits 53,658 peak tokens per second. QLoRA on 70B models works because the model fits in memory.

The AI BOX inherits whatever CUDA environment exists on the host machine. If your laptop runs Windows with CUDA properly configured, fine-tuning works. The RTX 5090's raw compute advantage means smaller model fine-tuning will be significantly faster, but you're limited to models that fit in 32 GB - which rules out full-precision fine-tuning of anything above 14B.

The thermal throttling issue on the DGX Spark deserves emphasis here. Multiple users, including John Carmack, reported throttling after 20-30 minutes of sustained load. NVIDIA has issued firmware patches, but the 150mm cube form factor has fundamental thermal limits. The AI BOX's 240mm AIO, despite being louder, handles sustained workloads more reliably.

Image Generation

For Stable Diffusion, FLUX, and ComfyUI workflows, the AI BOX wins outright. Image generation is GPU-compute and VRAM intensive, and 32 GB of GDDR7 at 1,792 GB/s is dramatically more capable than 128 GB of shared memory at 273 GB/s for this workload.

The AI BOX runs FLUX.1-dev at full precision without quantization. It generates batches of images quickly. The DGX Spark can run image generation too, but the bandwidth constraint makes it noticeably slower for the same models.

If image generation is a meaningful part of your workflow, this alone might decide the comparison.

The Convenience Factor

The DGX Spark has a significant advantage the spec sheet doesn't capture: it's a complete, pre-configured computer. Power it on, connect to its Wi-Fi hotspot, and you have a full CUDA development environment with Ollama pre-installed, Docker configured, JupyterLab ready, and DGX OS managing everything. It's truly plug-and-play for AI development.

The AI BOX is a peripheral. It requires a host laptop or desktop with Thunderbolt 5, a properly configured CUDA environment, compatible drivers, and - if you're on Linux - patience with eGPU hotplug behavior. It adds GPU power to a machine you already have. The DGX Spark is a machine.

For a developer who wants to buy one device and start running inference within an hour, the DGX Spark is significantly easier. For someone who already has a well-configured development laptop and just needs more GPU, the AI BOX slots in with less friction than building a separate desktop.

Noise, Heat, and Living With Them

The DGX Spark is meaningfully quieter. At 35 dB max it's barely audible. The AI BOX at 53 dB is clearly present in a room. If your setup is in a bedroom or shared office, this matters.

The heat profile is different but both have issues. The AI BOX gets hot on the outside (60C surface) but keeps the GPU at safe temperatures. The DGX Spark stays cool externally but can overheat internally under sustained loads. Pick your problem.

Who Should Buy Which

Buy the AORUS RTX 5090 AI BOX ($2,999) if you:

• Already own a Thunderbolt 5 laptop and want desktop GPU power
• Primarily run models under 30B parameters
• Need maximum inference throughput for interactive or batch applications
• Run image generation workloads (Stable Diffusion, FLUX, ComfyUI)
• Want sustained compute without thermal throttling concerns
• Are comfortable managing your own CUDA/driver setup
• Need the most compute per dollar

Buy the NVIDIA DGX Spark ($4,699) if you:

• Need to load models above 30B parameters at full precision
• Want a standalone, pre-configured AI development computer
• Value a complete software stack (DGX OS, Ollama, Docker, JupyterLab) out of the box
• Need the option to cluster two units for 256 GB and 235B+ models
• Work in a noise-sensitive environment
• Don't have a Thunderbolt 5 laptop or don't want to depend on one
• Need full torch.compile support without host OS dependencies

Buy neither if you:

• Need sustained multi-hour training on large models (get cloud GPUs)
• Can build a desktop with an RTX 5090 (you'll get 18-27% more performance)
• Need more than 128 GB for a single model (rent an H100 or A100 cluster)

The Bottom Line

These are not competing products. The AORUS AI BOX is a raw GPU accelerator that transforms a laptop into a workstation. The DGX Spark is a self-contained AI computer. The AI BOX is faster for anything that fits in 32 GB. The DGX Spark can load things the AI BOX physically cannot.

If you work with models in the 8B-14B range and need speed, the AI BOX delivers 6.5x more memory bandwidth at $1,700 less. If you work with 70B+ models and need them at full precision, the DGX Spark is the only option under $10K that doesn't involve cloud GPUs.

Most ML practitioners would be better served by the AI BOX plus quantization than by the DGX Spark's larger but slower memory - unless they specifically need to run large models unquantized or need the pre-configured software stack. The DGX Spark's real value is its unique position as the only sub-$5K device that fits a 70B model in memory with full CUDA support. If that specific capability matters to your work, nothing else comes close.

Sources

• AORUS RTX 5090 AI BOX Review - Awesome Agents
• DGX Spark Review - Awesome Agents
• AORUS RTX 5090 AI BOX Review - NotebookCheck
• DGX Spark In-Depth Review - LMSYS
• DGX Spark Performance - NVIDIA Technical Blog
• DGX Spark vs Mac Studio - Sebastian Raschka