Short answer: choose an RTX 5090 when the complete workload fits comfortably in 32GB and you want a conventional, upgradeable x86 CUDA workstation. Choose DGX Spark when model capacity beyond 32GB matters more than discrete-GPU bandwidth and your stack is ready for Arm Linux. DGX Spark’s 128GB is coherent unified system memory shared by the CPU, GPU, operating system and applications—not 128GB of dedicated VRAM.
The decision in one minute
- RTX 5090: 32GB GDDR7, 1,792GB/s published bandwidth and 575W reference total graphics power; a discrete desktop GPU for an x86 Windows or Linux host.
- DGX Spark: GB10 Grace Blackwell, 128GB LPDDR5x coherent unified system memory, 273GB/s and a 140W GB10 TDP; a complete Arm Linux system.
- Capacity result: DGX Spark can hold several cited 78–127GB model files that cannot fit in 32GB, but OS, runtime, cache and buffers reduce the memory available to a model.
- Speed result: capacity and peak FP4 figures do not rank local-LLM speed. Compare the same model, runtime, context and batch.
RTX 5090 vs DGX Spark specifications that matter
| System | Memory | Published bandwidth | Power figure | Platform |
|---|---|---|---|---|
| RTX 5090 Founders Edition | 32GB GDDR7 dedicated GPU memory | 1,792GB/s | 575W total graphics power | Active-cooled PCIe GPU; host supplied separately |
| NVIDIA DGX Spark | 128GB LPDDR5x coherent unified system memory | 273GB/s | 140W GB10 TDP | Complete GB10 Arm system running DGX OS |
Both products use NVIDIA’s Blackwell generation, but they solve different problems. RTX 5090 is a high-bandwidth discrete graphics card. DGX Spark integrates an Arm CPU and Blackwell GPU around one coherent memory pool. “CUDA” does not erase differences in architecture, operating system, kernels, application packaging or performance.
Exact Qwen3.5 model-file boundaries
The margins below subtract a cited artifact’s decimal file size from nominal memory. For DGX Spark this is deliberately labeled nominal system-memory arithmetic: the OS, CPU, runtime, KV cache, workspaces, multimodal state and other processes share the same 128GB. A positive number is not a deployment guarantee.
| Official or identified artifact | Files | RTX 5090 32GB | DGX Spark 128GB unified |
|---|---|---|---|
| Qwen3.5 27B Q4 + projector | 17.668GB | 14.332GB nominal margin | 110.332GB nominal system-memory margin |
| Qwen3.5 27B Q8 + projector | 29.523GB | 2.477GB — unsafe planning margin | 98.477GB nominal system-memory margin |
| Qwen3.5 27B BF16 | 55.563GB | No file fit | 72.437GB nominal system-memory margin |
| Qwen3.5 35B Q8 + projector | 37.802GB | No file fit | 90.198GB nominal system-memory margin |
| Qwen3.5 35B BF16 | 71.904GB | No file fit | 56.096GB nominal system-memory margin |
| Qwen3.5 122B official GPTQ-Int4 | 78.864GB | No file fit | 49.136GB nominal system-memory margin |
| Qwen3.5 122B community Q4_K_M + F16 projector | 77.446GB | No file fit | 50.554GB nominal system-memory margin |
| Qwen3.5 122B official FP8 | 127.163GB | No file fit | 0.837GB — file-only arithmetic is unsafe |
| Qwen3.5 122B community Q8_0 + F16 projector | 130.781GB | No file fit | No file fit |
| Qwen3.5 122B official BF16 | 250.173GB | No file fit | No file fit |
What 128GB unified memory changes
The 78.864GB GPTQ-Int4 and 77.446GB Q4-plus-projector artifacts become plausible single-system candidates on DGX Spark while exceeding RTX 5090’s physical VRAM. The 127.163GB FP8 file leaves only 0.837GB before accounting for the operating system or runtime, so it is not a safe 128GB plan. The 130.781GB Q8-plus-projector and 250.173GB BF16 artifacts do not pass even the file-size gate.
Bandwidth, platform and expansion tradeoffs
- Bandwidth: RTX 5090’s published 1,792GB/s is far above DGX Spark’s 273GB/s. Neither figure alone predicts tokens per second, but capacity should not be mistaken for speed.
- Compatibility: DGX Spark runs DGX OS on Arm. Verify every container, wheel, CUDA extension, inference runtime, desktop application and peripheral. An x86-only dependency may not run unchanged.
- Expansion: RTX 5090 is one component in a host you can configure and upgrade. DGX Spark is an integrated appliance-like system; evaluate its complete I/O, storage and service path.
- Multi-system: ConnectX-7 supports documented DGX Spark configurations, but multiple boxes do not automatically become one transparent memory pool. Software must distribute the model and communication adds overhead.
Choose the next evidence
| If this is your constraint | Start here |
|---|---|
| Your complete workload fits comfortably below 32GB | RTX 5090 requirements and offers |
| You need substantially more than 32GB in a compact CUDA system | DGX Spark and GX10 guide |
| You need a capacity estimate first | VRAM calculator and recommender |
| You need a reproducible pass/fail workload | Local-AI workload test |
Continue the decision
- Qwen3.5 27B memory · 35B-A3B memory · 122B-A10B memory
- 24GB, 32GB and 48GB tiers · 96GB-to-1TB hardware paths
- Screened offers · benchmark builder · source-reported benchmarks
- Save a private watchlist after confirming the right platform and capacity.
Official sources
- NVIDIA GeForce RTX 5090 specifications
- NVIDIA DGX Spark specifications
- NVIDIA DGX Spark hardware guide
Reviewed October 25, 2026. Specifications are manufacturer-published. HighVRAM has not independently benchmarked this pair. File arithmetic is not a runtime performance claim or fit guarantee.
Prefer another complete compact system? Compare DGX Spark with Mac Studio across 128GB versus 96GB–512GB unified memory and CUDA versus MLX.
Need a professional 96GB workstation alternative? Compare DGX Spark with RTX PRO 6000 Blackwell.
DGX Spark alternatives: Explore alternatives beyond the RTX 5090 pair in the constraint-first comparison hub.
DGX Spark vs H100: Need the data-center Hopper alternative to this desktop comparison? in the exact capacity and platform comparison.
For the preceding 24GB Ada desktop path, compare DGX Spark with RTX 4090 rather than assuming the 5090 result transfers unchanged.
For the lower-cost used 24GB generation, see DGX Spark vs RTX 3090.