Short answer: choose RTX PRO 6000 Blackwell Workstation Edition when the workload fits comfortably in 96GB and you need dedicated ECC GPU memory, an x86 CUDA workstation and professional NVIDIA support. Choose DGX Spark when a compact complete system and 128GB unified memory change what can fit—and your software supports its Arm Linux environment. The 32GB capacity difference is real, but 128GB shared system memory is not the same as 128GB dedicated VRAM.
The decision in one minute
- RTX PRO 6000: 96GB GDDR7 ECC, 1,792GB/s, 600W board power and a dual-slot workstation-card role.
- DGX Spark: 128GB LPDDR5x coherent unified system memory, 273GB/s, 140W GB10 TDP and a complete Arm Linux system.
- Capacity: DGX Spark passes the file gate for listed 122B FP8 where 96GB does not, but only 0.837GB remains before the OS or runtime—so it is still unsafe.
- Platform: decide between a conventional x86 CUDA workstation and an integrated Arm/DGX system before comparing specifications.
DGX Spark vs RTX PRO 6000 Blackwell specifications
| Factor | DGX Spark | RTX PRO 6000 Blackwell Workstation Edition |
|---|---|---|
| Memory | 128GB LPDDR5x coherent unified system memory | 96GB dedicated GDDR7 with ECC |
| Memory consumers | CPU, GPU, DGX OS and applications share one pool | GPU allocations use the 96GB pool; host RAM is separate |
| Published bandwidth | 273GB/s | 1,792GB/s |
| Published power scope | 140W GB10 TDP | 600W GPU total board power |
| Host/platform | Complete GB10 Arm system running DGX OS | PCIe 5.0 x16 GPU requiring a qualified x86 workstation |
| Form factor | Compact integrated system | 12-inch dual-slot extended-height card |
| Segmentation | One coherent shared pool | MIG support; exposed memory can be less than 96GB when partitioned |
Exact Qwen3.5 model-file boundaries
Margins below subtract cited decimal artifact size from the advertised capacity. RTX PRO figures are file-only device-memory margins. DGX Spark figures are file-only system-memory margins; the OS, CPU, runtime, cache, workspaces and other applications also use the 128GB pool.
| Artifact | Files | RTX PRO 6000 96GB | DGX Spark 128GB unified |
|---|---|---|---|
| Qwen3.5 35B Q8 + projector | 37.802GB | 58.198GB file-only margin | 90.198GB nominal system-memory margin |
| Qwen3.5 35B BF16 | 71.904GB | 24.096GB file-only margin | 56.096GB nominal system-memory margin |
| 122B official GPTQ-Int4 | 78.864GB | 17.136GB file-only margin | 49.136GB nominal system-memory margin |
| 122B Q4_K_M + F16 projector | 77.446GB | 18.554GB file-only margin | 50.554GB nominal system-memory margin |
| 122B official FP8 | 127.163GB | No full-device file fit | 0.837GB — unsafe system-memory boundary |
| 122B Q8_0 + F16 projector | 130.781GB | No full-device file fit | No file fit |
| 122B official BF16 | 250.173GB | No full-device file fit | No file fit |
What the extra 32GB changes
DGX Spark’s larger advertised pool improves nominal headroom for the listed 35B and 122B 4-bit artifacts. It does not make the 127.163GB FP8 artifact a sound plan: only 0.837GB remains before any system or runtime allocation. The 130.781GB Q8-plus-projector and 250.173GB BF16 artifacts exceed both devices’ single-system file gate.
Bandwidth, software and deployment safeguards
- Bandwidth is not speed: the 1,792GB/s versus 273GB/s specifications do not predict a token-rate ratio across different memory models and hosts.
- Arm compatibility: DGX Spark requires Arm64-compatible containers, wheels, binaries, extensions and management tooling.
- Blackwell support: RTX PRO 6000 still needs a compatible driver, CUDA toolkit, framework, quantization kernels and compiled extensions.
- MIG: an RTX PRO 6000 process may see a 24GB or 48GB slice instead of the full 96GB. Record the exposed device memory.
- Power scope: compare complete-system wall power under the same workload, not a 140W SoC TDP with a 600W GPU board figure.
- Complete cost: DGX Spark is a complete system; RTX PRO 6000 requires the host, PSU, cabling, cooling, storage and support around the card.
Choose the next evidence
| If this is your constraint | Start here |
|---|---|
| You need one dedicated 96GB professional CUDA GPU | RTX PRO 6000 guide |
| You need the 128GB compact-system class | DGX Spark and GX10 guide |
| You need to test an exact workload | Local-AI workload test |
| You need memory planning before choosing | memory calculator |
Continue the decision
- Qwen3.5 35B memory · 122B-A10B memory · 48GB–96GB tiers
- DGX Spark vs Mac Studio · RTX 5090 vs RTX PRO 6000
- Screened offers · benchmark builder · source-reported database
- Save a private watchlist after confirming the platform and capacity.
Official sources
- NVIDIA DGX Spark specifications
- NVIDIA DGX Spark hardware guide
- NVIDIA RTX PRO 6000 Blackwell specifications
- NVIDIA RTX PRO 6000 datasheet
Reviewed October 25, 2026. Specifications are manufacturer-published. HighVRAM has not independently benchmarked this pair. File fit does not establish compatibility, speed, quality, reliability or value.
DGX Spark alternatives: Compare other DGX Spark alternatives before committing to the 96GB workstation path in the constraint-first comparison hub.
DGX Spark vs H100: Need the Hopper accelerator alternative to the 96GB workstation path? in the exact capacity and platform comparison.
For the 24GB consumer-GPU boundary below the 96GB workstation tier, use DGX Spark vs RTX 4090.
For the 24GB used-consumer path below the 96GB workstation tier, use DGX Spark vs RTX 3090.