Short answer: choose DGX Spark when you want a complete compact NVIDIA system with 128GB of coherent unified system memory and your stack supports Arm Linux. Choose H100 when data-center CUDA deployment, HBM bandwidth, MIG and qualified server infrastructure matter more than compactness or headline capacity. H100 PCIe/SXM provides 80GB; H100 NVL provides 94GB. Neither should be compared with DGX Spark as if memory labels, power figures or purchase boundaries were equivalent.
The decision in one minute
- DGX Spark: 128GB shared LPDDR5x system memory, 273GB/s, 140W GB10 TDP, complete Arm/DGX OS system.
- H100 PCIe: 80GB HBM3, 2TB/s, 350W passive card for qualified servers.
- H100 SXM: 80GB HBM3, 3.35TB/s, up to 700W for HGX/DGX platforms—not a PCIe card.
- H100 NVL: 94GB HBM3, 3.9TB/s, 350–400W air-cooled PCIe accelerator for qualified systems.
DGX Spark vs H100 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 |
| H100 PCIe | 80GB HBM3 dedicated accelerator memory | 2TB/s | 350W card | Passive PCIe accelerator; directed server airflow required |
| H100 SXM | 80GB HBM3 dedicated accelerator memory | 3.35TB/s | Up to 700W, configurable | SXM module for qualified HGX/DGX platforms |
| H100 NVL | 94GB HBM3 dedicated accelerator memory | 3.9TB/s | 350–400W, configurable | Air-cooled PCIe accelerator for qualified systems |
Exact Qwen3.5 model-file boundaries
Margins below subtract cited decimal artifact size from advertised memory. H100 figures are file-only device-memory margins. DGX Spark figures are against total shared system memory and therefore overstate what remains available to the model after DGX OS, CPU work, runtime, cache and workspaces.
| Artifact | Files | H100 80GB | H100 NVL 94GB | DGX Spark 128GB unified |
|---|---|---|---|---|
| Qwen3.5 35B BF16 | 71.904GB | 8.096GB — validate runtime headroom | 22.096GB | 56.096GB nominal system margin |
| 122B official GPTQ-Int4 | 78.864GB | 1.136GB — unsafe | 15.136GB | 49.136GB nominal system margin |
| 122B Q4_K_M + F16 projector | 77.446GB | 2.554GB — unsafe | 16.554GB | 50.554GB nominal system margin |
| 122B official FP8 | 127.163GB | No file fit | No file fit | 0.837GB — unsafe system boundary |
| 122B Q8_0 + F16 projector | 130.781GB | No file fit | No file fit | No file fit |
| 122B official BF16 | 250.173GB | No file fit | No file fit | No file fit |
Capacity versus bandwidth is not a winner
DGX Spark has a larger advertised pool than any single H100 variant here, but its 273GB/s published bandwidth is far below H100’s 2TB/s to 3.9TB/s. That does not support a universal speed ratio: model architecture, kernels, precision, batch, context, host, interconnect and runtime build determine observed behavior. H100’s smaller dedicated pool can outperform on supported workloads while failing a file-size boundary that DGX Spark passes; DGX Spark can hold a larger file yet be limited by shared-memory headroom or software compatibility.
Platform safeguards before buying
- Arm64: verify every DGX Spark container, wheel, binary and CUDA extension on the Arm host.
- H100 PCIe: passive cooling requires directed server airflow; a PCIe connector does not make it a desktop card.
- H100 SXM: a bare module requires its matching HGX/DGX baseboard, cooling, power, firmware and interconnect.
- H100 NVL: confirm the exact 94GB variant, qualified host, bridge/topology, power and airflow.
- MIG: an H100 process may see only an assigned slice rather than 80GB or 94GB.
- Multi-device memory: NVLink/NVSwitch or multiple DGX Spark systems do not create one automatic transparent pool; the runtime must partition the workload.
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 Hopper HBM, MIG or data-center deployment | H100 PCIe/SXM/NVL guide |
| You need a broader shortlist | DGX Spark alternatives |
| You need a reproducible pass/fail workload | Local-AI workload test |
Continue the decision
- Qwen3.5 35B memory · 122B-A10B memory · 48GB–96GB tiers
- NVIDIA data-center tiers · DGX Spark vs RTX PRO 6000
- 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.
DGX Spark vs H200: Continue from H100 to the 141GB H200 comparison in the exact 128GB-versus-141GB comparison.
If the Hopper platform is outside the budget, compare the preceding Ampere tier in DGX Spark vs A100 without assuming lower capacity or an older generation means lower complete-system cost.
For a consumer desktop alternative below H100, compare DGX Spark vs RTX 4090 with exact 24GB/128GB file gates.