Published · Phase 1
DGX Spark vs RTX 5090
Choose RTX 5090 for speed and DGX Spark for capacity. If the chosen 4-bit model plus cache fits below roughly 28 GB, the 5090 is usually the better interactive machine; if it needs more, Spark's 128 GB makes the decision.
Editorial review: complete · Updated 2026-08-30
Quick verdict
Quick verdict guidance: Choose RTX 5090 for speed and DGX Spark for capacity. If the chosen 4-bit model plus cache fits below roughly 28 GB, the 5090 is usually the better interactive machine; if it needs more, Spark's 128 GB makes the decision.
Comparison table
| Configuration | Evidence state | Metric | Source |
|---|---|---|---|
| Qwen3.8-27B · NVIDIA DGX Spark public lab · vLLM | Measured · Grade A | 30.009033471349664 tokens/second | StackBench controlled lab approved source qwen38-dgx-spark-owner-archive-20260817 |
| Qwen3.8-27B · NVIDIA GeForce RTX 5090 controlled lab · NInfer | Measured · Grade A | 121.09509060076549 tokens/second | StackBench controlled lab approved source ninfer-qwen38-27b-nvfp4-rtx5090-owner-20260821 |
Memory / capacity
Memory / capacity guidance: The comparison is 32 GB of very fast GDDR7 against 128 GB of slower coherent LPDDR5X. Fit the complete workload—including KV cache and concurrency—before comparing token rates.
Observed LLM performance
Observed LLM performance guidance: For Qwen3.8-class 4-bit models, a tuned 5090 can reach roughly 70–130 tok/s while Spark examples sit around 25–40 tok/s. That gap narrows or reverses only when the 5090 spills weights/cache from VRAM.
Prefill vs decode
Prefill vs decode guidance: Long context can turn a fitting 5090 model into a capacity problem. Spark supports much larger caches, while the 5090 should use retrieval and bounded 8K–16K defaults.
Power
Power guidance: Spark's complete-system envelope is far lower than a 5090 workstation. The 5090 spends more power to produce much lower latency on fitting models.
Current market cost
Current market cost guidance: Compare complete systems. Spark includes CPU, memory, storage, and networking; a 5090 quote needs a suitable workstation around the card.
Which models fit
Which models fit guidance: The comparison is 32 GB of very fast GDDR7 against 128 GB of slower coherent LPDDR5X. Fit the complete workload—including KV cache and concurrency—before comparing token rates.
Who each option suits
Who each option suits guidance: 5090 suits latency-sensitive individuals and small services. Spark suits large-model exploration, long-context work, and compact always-on labs that value memory more than peak speed.
What stands out
Under 28 GB total: choose RTX 5090 for speed.
Over 32 GB or multiple resident models: choose DGX Spark for capacity.
Need both memory and bandwidth: price RTX PRO 6000.
Evidence limitations
- Do not compare a batched aggregate result on one platform with single-stream decode on the other.
- ARM64 compatibility and professional support can matter more than benchmark speed for a real deployment.
Planner CTA
Compare this recommendation against your model, context, concurrency, latency, and budget in the ComputeSage Planner.
Sources / methodology
Recommendations combine official specifications, public model/runtime documentation, adjacent benchmark observations, and clearly labeled engineering estimates. Review the ComputeSage evidence methodology.
- NVIDIA GeForce RTX 5090 specifications
- NVIDIA DGX Spark specifications
- Qwen3.8-27B model repository
- vLLM installation and hardware documentation
- llama.cpp project and backend documentation
- StackBench controlled lab approved source qwen38-dgx-spark-owner-archive-20260817
- StackBench controlled lab approved source ninfer-qwen38-27b-nvfp4-rtx5090-owner-20260821