NVIDIA H100VSDGX Spark
AI基准测试对决 2026
NVIDIA H100
Hopper80GB
$25,000-30000
企业级
Data Center
DGX Spark
Grace Blackwell128GB
$3,000-4000
企业级
Workstation
LLM Inference
| 模型 | NVIDIA H100 | DGX Spark | 胜者 |
|---|---|---|---|
Typhoon2.5-Qwen3-4B越高越好 | 9,931tok/s | 1,105tok/s | NVIDIA H100 |
GPT-OSS-20B越高越好 | 8,553tok/s | 1,094tok/s | NVIDIA H100 |
Qwen3-4B-Instruct-FP8越高越好 | N/A | N/A | N/A |
Vision-Language
| 模型 | NVIDIA H100 | DGX Spark | 胜者 |
|---|---|---|---|
Qwen3-VL-4B越高越好 | 7,790tok/s | 1,237tok/s | NVIDIA H100 |
Qwen3-VL-8B越高越好 | 7,035tok/s | 972tok/s | NVIDIA H100 |
Typhoon-OCR-3B越高越好 | 14,019tok/s | 696tok/s | NVIDIA H100 |
Image Generation
| 模型 | NVIDIA H100 | DGX Spark | 胜者 |
|---|---|---|---|
Qwen-Image越低越好 | 28.00sec | 98.00sec | NVIDIA H100 |
Qwen-Image-Edit越低越好 | 29.00sec | 105.00sec | NVIDIA H100 |
Video Generation
| 模型 | NVIDIA H100 | DGX Spark | 胜者 |
|---|---|---|---|
Wan2.2-5B越低越好 | 180.00sec | 825.00sec | NVIDIA H100 |
Wan2.2-14B越低越好 | 404.00sec | 2352.00sec | NVIDIA H100 |
Speech-to-Text
| 模型 | NVIDIA H100 | DGX Spark | 胜者 |
|---|---|---|---|
Typhoon-ASR越高越好 | 0.392xx realtime | 0.342xx realtime | NVIDIA H100 |
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
NVIDIA H100 wins 10 out of 10 benchmarks, excelling in LLM Inference and Vision-Language. Its HBM3 memory bandwidth provides a decisive advantage for AI inference workloads.
主要差异
- NVIDIA H100 uses Hopper architecture while DGX Spark uses Grace Blackwell
- NVIDIA H100's HBM3 memory provides exceptional bandwidth for AI workloads
LLM Inference
NVIDIA H100 wins in LLM inference because NVIDIA H100's superior memory bandwidth (3.4TB/s vs 273GB/s) enables faster token generation, and HBM3 memory provides exceptional bandwidth for memory-bound LLM operations.
Vision-Language
NVIDIA H100 excels at vision-language tasks due to higher memory bandwidth accelerates image token processing, and 4th Gen Tensor Cores accelerate cross-attention between visual and text features.
Image Generation
NVIDIA H100 leads in image generation because faster memory enables quicker diffusion iterations, and Hopper architecture optimizations accelerate denoising operations.
Video Generation
NVIDIA H100 dominates video generation with 3.4TB/s bandwidth handles high-throughput video data, and large VRAM capacity enables running advanced video generation models.
Speech-to-Text
NVIDIA H100 excels at speech-to-text because superior memory bandwidth enables faster audio feature processing, and 4th Gen Tensor Cores accelerate attention-based speech recognition.
技术规格
NVIDIA H100
DGX Spark
总体胜者
NVIDIA H100
10 胜出 10 benchmarks
10
NVIDIA H100
0
DGX Spark
NVIDIA H100 优势
- Strong in LLM Inference
- Dominates in Vision-Language
- Dominates in Image Generation
- Dominates in Video Generation
DGX Spark 优势
- More VRAM (128GB vs 80GB)
- Much lower power consumption
Frequently Asked Questions
NVIDIA H100 outperforms DGX Spark in 10 out of 10 AI benchmarks. The NVIDIA H100's Hopper architecture features the Transformer Engine with FP8 precision, specifically designed for large language models and transformer-based AI workloads. With 3.4 TB/s memory bandwidth and 80GB HBM3 memory, it delivers superior throughput for AI inference workloads.
NVIDIA H100 has 80GB of HBM3 memory with 3.4 TB/s bandwidth. DGX Spark has 128GB of LPDDR5X memory with 273 GB/s bandwidth. NVIDIA H100's HBM3 memory provides exceptional bandwidth for memory-bound AI workloads like LLM inference.
NVIDIA H100 is faster for LLM inference. LLM performance is heavily dependent on memory bandwidth - NVIDIA H100's 3.4 TB/s HBM3 enables faster token generation compared to DGX Spark's 273 GB/s.
NVIDIA H100 has a TDP of 700W 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.
NVIDIA H100 is priced around $25,000-30000 (enterprise/datacenter), while DGX Spark costs approximately $3,000-4000 (enterprise/datacenter).