RTX 5080VSNVIDIA H100
AI Benchmark Battle 2026
RTX 5080
Blackwell16GB
$999-1100
ผู้บริโภค
High-End
NVIDIA H100
Hopper80GB
$25,000-30000
องค์กร
Data Center
ระดับ Concurrency ต่างกัน
NVIDIA H100 ทดสอบที่ 128 concurrent requests (workload ระดับ datacenter) ขณะที่ RTX 5080 ทดสอบที่ 16 concurrent requests (โหลดทั่วไป) Concurrency สูงแสดง throughput แต่อาจไม่สะท้อน latency ของผู้ใช้เดี่ยว
LLM Inference
| โมเดล | RTX 5080 | NVIDIA H100 | ผู้ชนะ |
|---|---|---|---|
Typhoon2.5-Qwen3-4Bยิ่งสูงยิ่งดี | 1,013tok/s | 9,931tok/s | NVIDIA H100 |
GPT-OSS-20Bยิ่งสูงยิ่งดี | ไม่สามารถรันได้ | 8,553tok/s | NVIDIA H100 |
Qwen3-4B-Instruct-FP8ยิ่งสูงยิ่งดี | N/A | N/A | N/A |
Vision-Language
| โมเดล | RTX 5080 | NVIDIA H100 | ผู้ชนะ |
|---|---|---|---|
Qwen3-VL-4Bยิ่งสูงยิ่งดี | 895tok/s | 7,790tok/s | NVIDIA H100 |
Qwen3-VL-8Bยิ่งสูงยิ่งดี | 403tok/s | 7,035tok/s | NVIDIA H100 |
Typhoon-OCR-3Bยิ่งสูงยิ่งดี | 394tok/s | 14,019tok/s | NVIDIA H100 |
Image Generation
| โมเดล | RTX 5080 | NVIDIA H100 | ผู้ชนะ |
|---|---|---|---|
Qwen-Imageยิ่งต่ำยิ่งดี | 106.00sec | 28.00sec | NVIDIA H100 |
Qwen-Image-Editยิ่งต่ำยิ่งดี | 114.00sec | 29.00sec | NVIDIA H100 |
Video Generation
| โมเดล | RTX 5080 | NVIDIA H100 | ผู้ชนะ |
|---|---|---|---|
Wan2.2-5Bยิ่งต่ำยิ่งดี | 712.00sec | 180.00sec | NVIDIA H100 |
Wan2.2-14Bยิ่งต่ำยิ่งดี | 2067.00sec | 404.00sec | NVIDIA H100 |
Speech-to-Text
| โมเดล | RTX 5080 | NVIDIA H100 | ผู้ชนะ |
|---|---|---|---|
Typhoon-ASRยิ่งสูงยิ่งดี | 0.344xx realtime | 0.392xx realtime | NVIDIA H100 |
วิเคราะห์ผู้ชนะ
เจาะลึกว่าทำไม GPU แต่ละตัวมีประสิทธิภาพต่างกันตามสเปคเทคนิค
สรุปการวิเคราะห์ทางเทคนิค
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.
ความแตกต่างหลัก
- RTX 5080 uses Blackwell architecture while NVIDIA H100 uses Hopper
- NVIDIA H100's HBM3 memory provides exceptional bandwidth for AI workloads
- RTX 5080 offers consumer pricing vs NVIDIA H100's enterprise cost
- NVIDIA H100 has 80GB VRAM for larger models
LLM Inference
NVIDIA H100 wins in LLM inference because NVIDIA H100's superior memory bandwidth (3.4TB/s vs 960GB/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 more VRAM (80GB) handles larger image batches efficiently.
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 significantly more VRAM (80GB) maintains temporal coherence across frames, and 3.4TB/s bandwidth handles high-throughput video data.
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.
ข้อมูลจำเพาะทางเทคนิค
RTX 5080
NVIDIA H100
ผู้ชนะโดยรวม
NVIDIA H100
10 ชนะจาก 10 benchmarks
0
RTX 5080
10
NVIDIA H100
RTX 5080 ข้อได้เปรียบ
- Significantly lower cost
- Easier availability
NVIDIA H100 ข้อได้เปรียบ
- More VRAM (80GB vs 16GB)
- Strong in LLM Inference
- Dominates in Vision-Language
- Dominates in Image Generation
Frequently Asked Questions
NVIDIA H100 outperforms RTX 5080 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 5080 has 16GB of GDDR7 memory with 960 GB/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 5080's 960 GB/s.
RTX 5080 has a TDP of 360W while NVIDIA H100 has a TDP of 700W. RTX 5080 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 5080 is priced around $999-1100 (consumer market), while NVIDIA H100 costs approximately $25,000-30000 (enterprise/datacenter). Note that RTX 5080 is a consumer GPU while NVIDIA H100 is an enterprise solution with different support and warranty terms.