Short answer: choose the 128GB DGX Spark when your local-AI priority is fitting a larger model in one compact, complete system. Choose an A100 only when you already have—or can correctly qualify—the server, cooling, power and software path for a 40GB or 80GB data-center accelerator. The memory types, bandwidth figures and platform boundaries are different, so this is not a “128GB beats 80GB” speed claim.
The decision in one minute
- DGX Spark 128GB: complete desktop Arm system, 128GB coherent shared LPDDR5x, 273GB/s published bandwidth and 140W GB10 SoC TDP.
- A100 40GB: older Ampere data-center accelerator with 40GB dedicated HBM2, 1.6TB/s published bandwidth, passive cooling and PCIe or HGX/SXM deployment.
- A100 80GB: 80GB dedicated HBM2e, 1,935GB/s PCIe or 2,039GB/s SXM published bandwidth, with the same qualified-server requirement.
- Capacity result: the 128GB Spark passes several exact 122B 4-bit file-size gates that one 40GB or 80GB A100 does not. That does not establish faster inference.
DGX Spark 128GB vs A100 40GB and 80GB
| Boundary | DGX Spark 128GB | A100 40GB | A100 80GB PCIe / SXM |
|---|---|---|---|
| What it is | Complete compact desktop system | Passive data-center PCIe card or SXM accelerator | Data-center PCIe card or SXM accelerator |
| Memory | 128GB coherent unified system memory | 40GB dedicated HBM2 | 80GB dedicated HBM2e |
| Published bandwidth | 273GB/s | 1.6TB/s | 1,935GB/s PCIe; 2,039GB/s SXM |
| Published power boundary | 140W GB10 SoC TDP; 240W supplied PSU | 250W PCIe; 400W SXM | 300W PCIe; 400W standard SXM |
| CPU / software boundary | 20-core Arm CPU; DGX OS; verify Arm64 packages and containers | Host/server is separate; verify exact CUDA, driver, framework and Ampere kernel support | |
| Cooling / host | Integrated complete-system thermal design | Qualified server airflow/platform required; a bare listing is not a complete local-AI system | |
| MIG | Do not assume A100-style MIG behavior | Up to seven instances; fit must use memory exposed to the selected MIG profile, not headline card memory | |
Do not compare the power rows as wall-power measurements. NVIDIA defines the Spark number at the GB10 SoC and supplies a 240W system PSU; A100 figures are accelerator TDPs inside a server. Likewise, published bandwidth is not an application benchmark.
Exact Qwen3.5 file-size gates
This table subtracts listed artifact bytes from the advertised memory number only. Positive space is not proof that the runtime will load: the OS, runtime, KV cache, workspace, allocator behavior, projector and other processes still need memory. Spark margins are against shared system memory; A100 margins are against dedicated device memory.
| Exact artifact | Listed files | Spark 128GB | A100 40GB | A100 80GB |
|---|---|---|---|---|
| Qwen3.5-27B Q8_0 + projector | 29.523GB | 98.477GB nominal system margin | 10.477GB file-only device margin | 50.477GB file-only device margin |
| Qwen3.5-35B-A3B BF16 | 71.904GB | 56.096GB nominal system margin | No file-size fit | 8.096GB file-only device margin |
| Qwen3.5-122B-A10B official GPTQ | 78.864GB | 49.136GB nominal system margin | No file-size fit | 1.136GB unsafe boundary |
| Qwen3.5-122B-A10B Q4_K_M + projector | 77.446GB | 50.554GB nominal system margin | No file-size fit | 2.554GB unsafe boundary |
| Qwen3.5-122B-A10B FP8 | 127.163GB | 0.837GB unsafe shared-memory boundary | No file-size fit | No file-size fit |
| Qwen3.5-122B-A10B BF16 | 250.173GB | No one-device file-size fit | ||
For the underlying artifacts, see the 27B guide, 35B guide and 122B guide. File fit is not a quality, compatibility, latency, throughput or value ranking.
When DGX Spark is the defensible choice
- You need more than 80GB of addressable shared capacity in one compact system.
- You want a complete vendor-integrated desktop rather than sourcing a qualified accelerator server.
- Your runtime, containers and dependencies have been verified on Arm64 and DGX OS.
- You accept that 273GB/s published bandwidth is far below A100 HBM bandwidth and will measure your actual workload.
When A100 is the defensible choice
- Your workload fits the exact 40GB/80GB device or MIG slice with measured headroom.
- You already have a qualified server with correct passive airflow, power delivery, firmware and driver support.
- Dedicated HBM bandwidth, mature x86 data-center deployment or MIG isolation matters more than fitting a larger one-device checkpoint.
- You have verified the exact listing identity, capacity, form factor, health and seller—not merely “A100” in a title.
Used A100 checks that a listing cannot answer for you
- Confirm 40GB versus 80GB and PCIe versus SXM from identifiers and system inventory.
- Confirm that an SXM module includes a usable HGX/DGX platform; it is not a PCIe card.
- For PCIe cards, verify qualified directed airflow, power cabling and chassis clearance.
- Check ECC, health telemetry, firmware, visible memory and any active MIG partition.
- Price the host, CPU, RAM, storage, cooling and power—not only the accelerator.
- Run the same artifact, revision, runtime, context, concurrency, timing boundary and repetitions on both systems.
Measure the decision
Use the workload test, stage exact configurations in the hardware recommender, inspect the DGX Spark guide and A100 guide, then compare the broader DGX Spark alternatives and NVIDIA accelerator tiers.
Primary sources
- NVIDIA DGX Spark specifications
- NVIDIA DGX Spark hardware overview
- NVIDIA A100 40GB datasheet
- NVIDIA A100 80GB datasheet
Specification and artifact evidence checked October 25, 2026. Marketplace price and availability change independently.
For an actively cooled 24GB desktop-GPU path rather than a qualified A100 server, compare DGX Spark vs RTX 4090.