Flagship vs Data Center

RTX 5090VSNVIDIA H100

AI基准测试对决 2026

VS

RTX 5090

Blackwell
显存

32GB

价格

$1,999-2200

类型

消费级

等级

Flagship

TDP: 575W

NVIDIA H100

Hopper
显存

80GB

价格

$25,000-30000

类型

企业级

等级

Data Center

TDP: 700W
基准测试方法说明

不同并发级别

NVIDIA H100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

NVIDIA H100
Typhoon2.5-Qwen3-4B越高越好
NVIDIA H100
RTX 50901,446tok/s
NVIDIA H1009,931tok/s
GPT-OSS-20B越高越好
NVIDIA H100
RTX 50901,338tok/s
NVIDIA H1008,553tok/s
Qwen3-4B-Instruct-FP8越高越好
N/A
RTX 5090N/A
NVIDIA H100N/A

Vision-Language

NVIDIA H100
Qwen3-VL-4B越高越好
NVIDIA H100
RTX 50901,005tok/s
NVIDIA H1007,790tok/s
Qwen3-VL-8B越高越好
NVIDIA H100
RTX 5090868tok/s
NVIDIA H1007,035tok/s
Typhoon-OCR-3B越高越好
NVIDIA H100
RTX 50901,577tok/s
NVIDIA H10014,019tok/s

Image Generation

NVIDIA H100
Qwen-Image越低越好
NVIDIA H100
RTX 509046.00sec
NVIDIA H10028.00sec
Qwen-Image-Edit越低越好
NVIDIA H100
RTX 509050.00sec
NVIDIA H10029.00sec

Video Generation

NVIDIA H100
Wan2.2-5B越低越好
NVIDIA H100
RTX 5090344.00sec
NVIDIA H100180.00sec
Wan2.2-14B越低越好
NVIDIA H100
RTX 5090903.00sec
NVIDIA H100404.00sec

Speech-to-Text

NVIDIA H100
Typhoon-ASR越高越好
NVIDIA H100
RTX 50900.324xx realtime
NVIDIA H1000.392xx realtime

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

  • RTX 5090 uses Blackwell architecture while NVIDIA H100 uses Hopper
  • NVIDIA H100's HBM3 memory provides exceptional bandwidth for AI workloads
  • RTX 5090 offers consumer pricing vs NVIDIA H100's enterprise cost
  • NVIDIA H100 has 80GB VRAM for larger models

LLM Inference

NVIDIA H100

NVIDIA H100 wins in LLM inference because NVIDIA H100's superior memory bandwidth (3.4TB/s vs 1.8TB/s) enables faster token generation, and HBM3 memory provides exceptional bandwidth for memory-bound LLM operations.

关键规格
RTX 5090|NVIDIA H100
Memory Bandwidth
1.8TB/s|3.4TB/s
VRAM
32GB|80GB
Memory Type
GDDR7|HBM3 (High Bandwidth)
Tensor Cores
5th Gen|4th Gen

Vision-Language

NVIDIA H100

NVIDIA H100 excels at vision-language tasks due to higher memory bandwidth accelerates image token processing, and more VRAM (80GB) handles larger image batches efficiently.

关键规格
RTX 5090|NVIDIA H100
Memory Bandwidth
1.8TB/s|3.4TB/s
VRAM
32GB|80GB
Memory Type
GDDR7|HBM3 (High Bandwidth)
Tensor Cores
5th Gen|4th Gen

Image Generation

NVIDIA H100

NVIDIA H100 leads in image generation because faster memory enables quicker diffusion iterations, and Hopper architecture optimizations accelerate denoising operations.

关键规格
RTX 5090|NVIDIA H100
Memory Bandwidth
1.8TB/s|3.4TB/s
VRAM
32GB|80GB
Memory Type
GDDR7|HBM3 (High Bandwidth)
Tensor Cores
5th Gen|4th Gen

Video Generation

NVIDIA H100

NVIDIA H100 dominates video generation with significantly more VRAM (80GB) maintains temporal coherence across frames, and 3.4TB/s bandwidth handles high-throughput video data.

关键规格
RTX 5090|NVIDIA H100
Memory Bandwidth
1.8TB/s|3.4TB/s
VRAM
32GB|80GB
Memory Type
GDDR7|HBM3 (High Bandwidth)
Tensor Cores
5th Gen|4th Gen

Speech-to-Text

NVIDIA H100

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.

关键规格
RTX 5090|NVIDIA H100
Memory Bandwidth
1.8TB/s|3.4TB/s
VRAM
32GB|80GB
Memory Type
GDDR7|HBM3 (High Bandwidth)
Tensor Cores
5th Gen|4th Gen

技术规格

RTX 5090

架构Blackwell
显存带宽1.8TB/s
显存类型GDDR7
显存32GB
DLSS 4Multi Frame GenerationNVLink Support

NVIDIA H100

架构Hopper
显存带宽3.4TB/s
显存类型HBM3
显存80GB
Transformer EngineFP8 SupportNVLink 4.0

总体胜者

NVIDIA H100

10 胜出 10 benchmarks

0

RTX 5090

10

NVIDIA H100

RTX 5090 优势

  • Significantly lower cost
  • Easier availability

NVIDIA H100 优势

  • More VRAM (80GB vs 32GB)
  • Strong in LLM Inference
  • Dominates in Vision-Language
  • Dominates in Image Generation

Frequently Asked Questions

NVIDIA H100 outperforms RTX 5090 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.

RTX 5090 has 32GB of GDDR7 memory with 1.8 TB/s bandwidth. NVIDIA H100 has 80GB of HBM3 memory with 3.4 TB/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 RTX 5090's 1.8 TB/s.

RTX 5090 has a TDP of 575W while NVIDIA H100 has a TDP of 700W. RTX 5090 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 NVIDIA H100 costs approximately $25,000-30000 (enterprise/datacenter). Note that RTX 5090 is a consumer GPU while NVIDIA H100 is an enterprise solution with different support and warranty terms.

试用Float16 GPU云

Run your AI workloads on high-performance GPUs with Float16 Cloud.