EDGE OSAI HARDWARE COMPARISON
DGX Spark vs RTX 5090Specs, memory and LLM performance
The RTX 5090 has 6.5× the bandwidth (1,792 GB/s vs 273 GB/s), so it generates tokens much faster, but only 32 GB of memory. DGX Spark has 128 GB, so it can hold models about four times larger.
Memory
128 GB vs 32 GB
Bandwidth
273 GB/s vs 1.79 TB/s
Power
240 W power supply vs 575 W
Type
Desktop AI system vs Consumer GPU
Which should you choose?
CHOOSE DGX SPARK IF
You want to run 70B to 170B-parameter models at 4-bit on one quiet, low-power desktop.
CHOOSE RTX 5090 IF
Your models fit in 32 GB (up to about 40B parameters at 4-bit) and you want the fastest responses for the money.
Still deciding between DGX Spark and RTX 5090?
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DGX Spark vs RTX 5090 specs
Manufacturer specifications. Green marks the stronger figure where a higher number is better. Dense compute is shown without sparsity.
| Specification | NVIDIA DGX Spark | NVIDIA GeForce RTX 5090 |
|---|---|---|
| Type | Desktop AI system | Consumer GPU |
| Memory | 128 GB | 32 GB |
| Memory type | LPDDR5X unified memory | GDDR7 |
| Memory bandwidth | 273 GB/s | 1.79 TB/s |
| FP16 / BF16 (dense) | Not published | Not published |
| FP8 (dense) | Not published | Not published |
| FP4 (dense) | 500 TFLOPS | 1,676 TFLOPS |
| Power | 240 W power supply | 575 W |
| Multi-GPU link | ConnectX-7 200 Gb/s: link two units for 256 GB | PCIe Gen5 x16 (no NVLink) |
| Form factor | Palm-sized desktop with 4 TB NVMe | Desktop graphics card |
| Architecture | GB10 Grace Blackwell, 2025 | Blackwell, 2025 |
| Software | CUDA, NVIDIA AI software, vLLM, llama.cpp, Ollama | CUDA, vLLM, llama.cpp, Ollama |
| Price | US$3,999 at launch (4 TB) | US$1,999 launch price (Founders Edition) |
What they mean for running LLMs
For large language models, memory decides which models fit and how many users you can serve, and memory bandwidth decides how fast each user gets tokens. These are planning figures for one GPU or system using a 4-bit model.
| For LLMs | DGX Spark | RTX 5090 |
|---|---|---|
| Usable for models | About 109 GB | About 29 GB |
| Largest dense model at 4-bit (1 user, short context) | About 170B parameters | About 41B parameters |
| Llama 3.3 70B at 4-bit, single-user speed ceiling | About 7 tokens/s | Does not fit on one GPU |
Which open-weight models fit
Memory needed for one user with 8K context at the lowest precision available for each model. Open a model for team-size and fine-tuning figures.
| Model (1 user, 8K context, lowest available precision) | DGX Spark | RTX 5090 |
|---|---|---|
| gpt-oss-20b 21B · about 15 GB at MXFP4 | Yes | Yes |
| Gemma 4 26B A4B 25.2B · about 16 GB at 4-bit | Yes | Yes |
| Qwen3.6-27B 27.78B · about 20 GB at 4-bit | Yes | Yes |
| Qwen3.8-27B 27.8B · about 24 GB at NVFP4 | Yes | Yes |
| Gemma 4 31B 30.7B · about 20 GB at 4-bit | Yes | Yes |
| Qwen3.6-35B-A3B 35B · about 24 GB at NVFP4 | Yes | Yes |
| Llama 3.3 70B Instruct 70.6B · about 46 GB at INT4 | Yes | 2 GPUs |
| gpt-oss-120b 117B · about 70 GB at MXFP4 | Yes | 4 GPUs |
| MiniMax-M2.5 229B · about 135 GB at 4-bit | 2 linked units | 8 GPUs |
| GLM-5.3-Flash 320B · about 192 GB at NVFP4 | 2 linked units | 8 GPUs |
| DeepSeek-V4.1-Flash 552B · about 320 GB at 4-bit | No | No: more than 8 GPUs |
| DeepSeek-R1 671B · about 410 GB at 4-bit | No | No: more than 8 GPUs |
| DeepSeek-V3.2 685B · about 419 GB at NVFP4 | No | No: more than 8 GPUs |
| GLM-5.2 744B · about 455 GB at NVFP4 | No | No: more than 8 GPUs |
| GLM-5.3 753B · about 460 GB at NVFP4 | No | No: more than 8 GPUs |
| GLM-5.1 754B · about 461 GB at NVFP4 | No | No: more than 8 GPUs |
| Kimi K2 1T · about 579 GB at 4-bit | No | No: more than 8 GPUs |
| DeepSeek-V4-Pro 1.6T · about 976 GB at FP4 + FP8 | No | No: more than 8 GPUs |
| Kimi K3 2.8T · about 1.62 TB at MXFP4 | No | No: more than 8 GPUs |
Models in amber need several of these GPUs working as one. Buying the GPUs is the easy part. Vanik Edge OS splits the model across them, keeps it running if a server fails and manages it all from one console. See how Edge OS clusters GPU servers
FROM SIZING TO RUNNING
Sizing the hardware is the easy part.
Getting several GPUs to work as one system for a whole company is where most projects slow down. That is the part Vanik Edge OS does for you.
STEP 01 · THIS TOOL
Size it
Work out the GPU memory your models need and which GPUs or servers can hold them.
STEP 02 · VANIK EDGE OS
Cluster it
Edge OS joins your GPU servers into one cluster, splits a large model across them and keeps it running if a server fails. NVIDIA and AMD servers work side by side.
STEP 03 · VANIK EDGE OS
Run it
Your team gets AI apps with single sign-on and an audit trail, managed from one console, with your data staying inside your building.
DGX Spark vs RTX 5090 questions
What is the difference between the DGX Spark and the RTX 5090?
The RTX 5090 has 6.5× the bandwidth (1,792 GB/s vs 273 GB/s), so it generates tokens much faster, but only 32 GB of memory. DGX Spark has 128 GB, so it can hold models about four times larger.
DGX Spark vs RTX 5090: which is better for LLM inference?
For generating tokens, the RTX 5090 is faster: it has 1.79 TB/s of memory bandwidth versus 273 GB/s, and LLM token generation is mostly limited by bandwidth. The DGX Spark holds larger models and more users’ context with 128 GB of memory versus 32 GB. Choose the DGX Spark if you want to run 70B to 170B-parameter models at 4-bit on one quiet, low-power desktop. Choose the RTX 5090 if your models fit in 32 GB (up to about 40B parameters at 4-bit) and you want the fastest responses for the money.
Can the DGX Spark and RTX 5090 run Llama 3.3 70B?
Llama 3.3 70B needs about 46 GB of memory at 4-bit for one user with 8K context. The DGX Spark runs it on one unit at about 7 tokens/s at most for one user, and the RTX 5090 needs 2 GPUs. Speeds are theoretical ceilings from memory bandwidth; real speeds are usually 50 to 80% of that.
How much does the DGX Spark cost compared with the RTX 5090?
DGX Spark: US$3,999 at launch (4 TB). RTX 5090: US$1,999 launch price (Founders Edition). Data center GPUs are usually bought as part of 4- or 8-GPU servers, so compare full-system quotes.
How are these numbers calculated?
Memory, bandwidth, compute and power are manufacturer specifications. Model memory needs come from our VRAM calculator: weights plus KV cache plus about 5 to 10% runtime overhead, keeping 10% of each GPU free (15% on GB10 systems, 25% on Mac). The speed ceiling is memory bandwidth divided by the model’s weight size. All figures are planning estimates.
Related comparisons
Specifications from the manufacturer pages for the NVIDIA DGX Spark and NVIDIA GeForce RTX 5090. Speed ceilings are bandwidth divided by model size, before batching; real throughput depends on the inference engine and settings. Last checked November 10, 2026.
Not sure which hardware fits your team?
Vanik Edge OS runs open-weight models on NVIDIA DGX, AMD Instinct and other GPU servers on your premises, and helps you size the hardware first.