RTX 5090VSDGX Spark
AI基准测试对决 2026
RTX 5090
Blackwell32GB
$1,999-2200
消费级
Flagship
DGX Spark
Grace Blackwell128GB
$3,000-4000
企业级
Workstation
不同并发级别
DGX Spark在128并发请求下测试（数据中心负载），而RTX 5090在16并发请求下测试（典型工作站负载）。高并发显示吞吐量能力，但可能不反映单用户延迟。
LLM Inference
| 模型 | RTX 5090 | DGX Spark | 胜者 |
|---|---|---|---|
Typhoon2.5-Qwen3-4B越高越好 | 1,446tok/s | 1,105tok/s | RTX 5090 |
GPT-OSS-20B越高越好 | 1,338tok/s | 1,094tok/s | RTX 5090 |
Qwen3-4B-Instruct-FP8越高越好 | N/A | N/A | N/A |
Vision-Language
| 模型 | RTX 5090 | DGX Spark | 胜者 |
|---|---|---|---|
Qwen3-VL-4B越高越好 | 1,005tok/s | 1,237tok/s | DGX Spark |
Qwen3-VL-8B越高越好 | 868tok/s | 972tok/s | DGX Spark |
Typhoon-OCR-3B越高越好 | 1,577tok/s | 696tok/s | RTX 5090 |
Image Generation
| 模型 | RTX 5090 | DGX Spark | 胜者 |
|---|---|---|---|
Qwen-Image越低越好 | 46.00sec | 98.00sec | RTX 5090 |
Qwen-Image-Edit越低越好 | 50.00sec | 105.00sec | RTX 5090 |
Video Generation
| 模型 | RTX 5090 | DGX Spark | 胜者 |
|---|---|---|---|
Wan2.2-5B越低越好 | 344.00sec | 825.00sec | RTX 5090 |
Wan2.2-14B越低越好 | 903.00sec | 2352.00sec | RTX 5090 |
Speech-to-Text
| 模型 | RTX 5090 | DGX Spark | 胜者 |
|---|---|---|---|
Typhoon-ASR越高越好 | 0.324xx realtime | 0.342xx realtime | DGX Spark |
赢家分析
深入了解每款GPU基于技术规格的性能差异原因
技术分析摘要
RTX 5090 wins 7 out of 10 benchmarks, excelling in LLM Inference and Image Generation. Its exceptional memory bandwidth provides a decisive advantage for AI inference workloads.
主要差异
- RTX 5090 uses Blackwell architecture while DGX Spark uses Grace Blackwell
- RTX 5090 features next-gen GDDR7 memory
- RTX 5090 offers consumer pricing vs DGX Spark's enterprise cost
- DGX Spark has 128GB VRAM for larger models
LLM Inference
RTX 5090 wins in LLM inference because RTX 5090's superior memory bandwidth (1.8TB/s vs 273GB/s) enables faster token generation, and Blackwell architecture delivers significant AI performance improvements.
Vision-Language
DGX Spark excels at vision-language tasks due to more VRAM (128GB) handles larger image batches efficiently, and 5th Gen Tensor Cores accelerate cross-attention between visual and text features.
Image Generation
RTX 5090 leads in image generation because faster memory enables quicker diffusion iterations, and Blackwell architecture optimizations accelerate denoising operations.
Video Generation
RTX 5090 dominates video generation with 1.8TB/s bandwidth handles high-throughput video data, and large VRAM capacity enables running advanced video generation models.
Speech-to-Text
DGX Spark excels at speech-to-text because 5th Gen Tensor Cores accelerate attention-based speech recognition.
技术规格
RTX 5090
DGX Spark
总体胜者
RTX 5090
7 胜出 10 benchmarks
7
RTX 5090
3
DGX Spark
RTX 5090 优势
- Significantly lower cost
- Easier availability
- Strong in LLM Inference
- Dominates in Image Generation
DGX Spark 优势
- More VRAM (128GB vs 32GB)
- Strong in Vision-Language
- Dominates in Speech-to-Text
Frequently Asked Questions
RTX 5090 outperforms DGX Spark in 7 out of 10 AI benchmarks. The RTX 5090's Blackwell architecture introduces 5th generation Tensor Cores with enhanced AI processing capabilities and DLSS 4 Multi Frame Generation. With 1.8 TB/s memory bandwidth and 32GB GDDR7 memory, it delivers superior throughput for AI inference workloads.
RTX 5090 has 32GB of GDDR7 memory with 1.8 TB/s bandwidth. DGX Spark has 128GB of LPDDR5X memory with 273 GB/s bandwidth. Higher memory bandwidth generally results in faster token generation for large language models.
RTX 5090 is faster for LLM inference. LLM performance is heavily dependent on memory bandwidth - RTX 5090's 1.8 TB/s GDDR7 enables faster token generation compared to DGX Spark's 273 GB/s.
RTX 5090 has a TDP of 575W while DGX Spark has a TDP of 300W. DGX Spark is more power efficient, making it suitable for deployments with power constraints. For cloud deployments, consider Float16.cloud where you can access these GPUs without managing power infrastructure.
RTX 5090 is priced around $1,999-2200 (consumer market), while DGX Spark costs approximately $3,000-4000 (enterprise/datacenter). Note that RTX 5090 is a consumer GPU while DGX Spark is an enterprise solution with different support and warranty terms.