Short answer: choose an RTX 4090 when the exact workload fits 24GB and you want a replaceable x86 desktop GPU with high published bandwidth. Choose DGX Spark 128GB when one compact complete system must hold a much larger artifact and its runtime is verified on Arm64/DGX OS. Capacity fit does not prove speed, quality or value.
The decisive difference
- RTX 4090: 24GB dedicated GDDR6X, 1,008GB/s published bandwidth, 450W reference TGP, active desktop cooling and no NVLink.
- DGX Spark 128GB: 128GB coherent shared LPDDR5x, 273GB/s published bandwidth, 140W GB10 SoC TDP and a supplied 240W PSU in a complete Arm/DGX OS system.
- Practical split: the 4090 is a 24GB accelerator inside a separately configured PC; Spark is a complete 128GB system whose memory is shared with the CPU, OS and runtime.
RTX 4090 vs DGX Spark specifications
| Boundary | RTX 4090 | DGX Spark 128GB |
|---|---|---|
| Product role | Replaceable desktop graphics card | Complete compact personal-AI system |
| Architecture | Ada Lovelace; CUDA capability 8.9 | Grace Blackwell GB10 with 20-core Arm CPU |
| Advertised memory | 24GB dedicated GDDR6X | 128GB coherent unified system memory |
| Published memory bandwidth | 1,008GB/s | 273GB/s |
| Published power boundary | 450W reference total graphics power; 850W recommended system power | 140W GB10 SoC TDP; 240W supplied system PSU |
| Cooling / chassis | Partner/reference active card; verify board dimensions, connector and airflow | Integrated 150mm × 150mm × 50.5mm system thermal design |
| CPU / software | Host PC is separate; mature x86 desktop path | DGX OS on Arm64; verify packages, containers and kernels |
| Multi-device | No NVLink; separate device-local pools | ConnectX-7 networking can connect systems, but software must explicitly distribute work |
Do not turn the bandwidth or power rows into a benchmark. Bandwidth is a peak memory-system specification. RTX 4090 TGP, recommended PC power and GB10 SoC TDP are different boundaries; neither is measured wall power for the same workload.
Exact Qwen3.5 file-size gates
The arithmetic below subtracts listed artifact files from advertised memory only. Positive space is not proof of a successful load: the runtime, KV/hybrid state, projector, visual tokens, compute workspace, allocator, display use and OS still need memory. RTX margins are dedicated device-memory arithmetic; Spark margins are nominal shared-system-memory arithmetic.
| Exact artifact | Listed files | RTX 4090 24GB | DGX Spark 128GB |
|---|---|---|---|
| Qwen3.5-27B community Q4_K_M + F16 projector | 17.668GB | 6.332GB file-only device margin | 110.332GB nominal system margin |
| Qwen3.5-27B community Q8_0 + projector | 29.523GB | No file-size fit | 98.477GB nominal system margin |
| Qwen3.5-27B official BF16 | 55.563GB | No file-size fit | 72.437GB nominal system margin |
| Qwen3.5-35B-A3B community Q4_K_M + projector | 22.915GB | 1.085GB unsafe boundary | 105.085GB nominal system margin |
| Qwen3.5-35B-A3B official GPTQ-Int4 | 24.420GB | No file-size fit | 103.580GB nominal system margin |
| Qwen3.5-122B-A10B official GPTQ | 78.864GB | No file-size fit | 49.136GB nominal system margin |
| Qwen3.5-122B-A10B FP8 | 127.163GB | No file-size fit | 0.837GB unsafe shared-memory boundary |
Use the exact 27B, 35B-A3B and 122B-A10B guides to preserve artifact provenance and cache/runtime caveats.
Choose RTX 4090 when
- Your exact model, context, modality and concurrency fit 24GB with measured headroom.
- You already own a compatible x86 PC and can verify PSU, connector, case clearance and cooling.
- You need a replaceable GPU for gaming, rendering or local AI rather than a dedicated appliance.
- Your software path benefits from Ada/x86 maturity and does not need more than 24GB device-local capacity.
Choose DGX Spark when
- Your exact artifact exceeds 24GB but fits a 128GB shared-memory system with real runtime headroom.
- You want a compact, integrated NVIDIA AI system rather than a separately assembled desktop.
- Your framework, packages and containers work correctly on Arm64/DGX OS.
- You accept that larger capacity and lower published bandwidth create a workload-specific tradeoff that must be measured.
Purchase and migration checks
- Choose the exact artifact, revision, projector and runtime before hardware.
- For RTX 4090, verify exact partner board, condition, warranty, connector history, PSU and physical fit.
- For Spark, verify 128GB configuration, storage, DGX OS version and Arm64 package/container availability.
- Record offload, visible memory, peak device or unified memory, context, concurrency and visual inputs.
- Run identical prompts and timing definitions; do not compare unrelated public token-rate screenshots.
- Price the entire PC or complete Spark system, not a card against a complete computer.
Continue the decision
Inspect the RTX 4090 product page, DGX Spark guide, 3090 vs 4090 vs 5090 comparison and DGX Spark alternatives hub. Stage the intended workload in the workload test and hardware recommender.
Primary sources
- NVIDIA GeForce RTX 4090 specifications
- NVIDIA Ada GPU architecture white paper
- NVIDIA DGX Spark specifications
- NVIDIA DGX Spark hardware overview
Specification and artifact evidence checked October 25, 2026. HighVRAM has not converted these peak specifications into performance claims. Marketplace price, condition and availability change independently.
For the older, often-used 24GB Ampere path with optional NVLink, compare DGX Spark vs RTX 3090.