Short answer: choose DGX Spark when you need a complete compact NVIDIA system with 128GB of coherent unified system memory and an Arm/DGX OS software path. Choose H200 when 141GB of dedicated HBM3e, 4.8TB/s published bandwidth, MIG and data-center deployment justify a qualified server platform. The headline capacity difference is only 13GB, but the memory model, bandwidth, power, cooling and purchase boundary are fundamentally different.
The decision in one minute
- DGX Spark: 128GB LPDDR5x shared system memory, 273GB/s, 140W GB10 TDP and a complete compact Arm system.
- H200 SXM: 141GB HBM3e, 4.8TB/s, up to 700W and an HGX-class platform.
- H200 NVL: 141GB HBM3e, 4.8TB/s, up to 600W and a qualified air-cooled PCIe/MGX system.
- Critical boundary: the listed 122B FP8 file is unsafe on 128GB shared memory but leaves 13.837GB file-only margin on H200; Q8-plus-projector does not fit 128GB but leaves 10.219GB on H200.
DGX Spark vs H200 specifications that matter
| System/device | Memory | Published bandwidth | Power scope | Deployment |
|---|---|---|---|---|
| DGX Spark | 128GB LPDDR5x coherent unified system memory | 273GB/s | 140W GB10 TDP | Complete compact Arm system running DGX OS |
| H200 SXM | 141GB HBM3e dedicated accelerator memory | 4.8TB/s | Up to 700W, configurable | SXM module for qualified HGX H200 systems |
| H200 NVL | 141GB HBM3e dedicated accelerator memory | 4.8TB/s | Up to 600W, configurable | Dual-slot air-cooled PCIe for qualified MGX/NVIDIA-Certified systems |
Exact Qwen3.5 model-file boundaries
Margins subtract cited decimal file size from advertised memory. H200 figures are file-only device-memory margins. DGX Spark figures are against shared system memory before DGX OS, CPU work, runtime, cache, buffers and other applications.
| Artifact | Files | DGX Spark 128GB unified | H200 141GB |
|---|---|---|---|
| Qwen3.5 35B BF16 | 71.904GB | 56.096GB nominal system margin | 69.096GB file-only margin |
| 122B official GPTQ-Int4 | 78.864GB | 49.136GB nominal system margin | 62.136GB file-only margin |
| 122B Q4_K_M + F16 projector | 77.446GB | 50.554GB nominal system margin | 63.554GB file-only margin |
| 122B official FP8 | 127.163GB | 0.837GB — unsafe system boundary | 13.837GB — validate runtime headroom |
| 122B Q8_0 + F16 projector | 130.781GB | No file fit | 10.219GB — validate runtime headroom |
| 122B official BF16 | 250.173GB | No file fit | No file fit |
What the extra 13GB changes
H200’s 141GB crosses the listed 122B Q8-plus-projector file boundary that DGX Spark’s 128GB pool does not. It also increases the listed FP8 remainder from an unusable 0.837GB system-memory margin to 13.837GB of file-only device margin. Neither remainder proves the desired context, cache precision, batching or concurrency fits. The 250.173GB BF16 checkpoint exceeds both.
Bandwidth, platform and acquisition safeguards
- Bandwidth is not a token-rate ratio: 4.8TB/s versus 273GB/s does not determine application speed without a controlled workload.
- Arm64: DGX Spark requires compatible containers, wheels, binaries and extensions on its Arm host.
- SXM: a bare H200 SXM module cannot be installed in an ordinary PCIe workstation; it requires the matching baseboard, cooling, power, firmware and interconnect.
- H200 NVL: PCIe form factor still requires a qualified host, directed airflow, auxiliary power and correct firmware/topology.
- MIG: a process can see only an assigned slice rather than the full 141GB.
- Complete cost: compare a ready-to-use DGX Spark with a complete deployable H200 server—not a bare accelerator listing.
Choose the next evidence
| If this is your constraint | Start here |
|---|---|
| You need a compact complete 128GB NVIDIA system | DGX Spark and GX10 guide |
| You need one 141GB Hopper accelerator | H200 SXM/NVL guide |
| You need the broader alternative set | DGX Spark alternatives |
| You need a repeatable pass/fail workload | Local-AI workload test |
Continue the decision
- Qwen3.5 35B memory · 122B-A10B memory · DGX Spark vs H100
- NVIDIA data-center tiers · H100 vs H200
- Memory calculator · screened offers · benchmark builder
- Source-reported database · private watchlist
Official sources
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.
For the older 40GB/80GB Ampere alternative, continue with DGX Spark vs A100; it keeps passive cooling, SXM/PCIe, MIG and exact file margins visible.
For the mainstream 24GB desktop alternative, see DGX Spark vs RTX 4090.