Hosting DeepSeek-r1 14b: Choosing the Right GPU Server
The rise of advanced language models like DeepSeek-r1 14b (9GB, Q4) has brought about a demand for powerful GPU servers to achieve efficient performance. In this article, we compare two GPU server configurations for hosting DeepSeek-r1, evaluate their performance metrics, and provide insights into choosing the ideal solution.
Results: Benchmarking DeepSeek-r1 14b on Ollama
| GPU Servers | GPU VPS - A4000 | GPU Dedicated Server - P100 |
|---|---|---|
| Server Configs | 32GB RAM 24 CPU Cores 320GB SSD 300Mbps Unmetered Bandwidth OS: Ubuntu 24.0 GPU: Quadro RTX A4000 Order Now > | 128GB RAM Dual 10-Core E5-2660v2 120GB + 960GB SSD 100Mbps-1Gbps OS: Ubuntu 24.0 GPU: Nvidia Tesla P100 Order Now > |
| Platform | Ollama0.5.7 | Ollama0.5.7 |
| Model | Deepseek-R1, 14b, 9GB, Q4 | Deepseek-R1, 14b, 9GB, Q4 |
| Downloading Speed(MB/s) | 36 | 11 |
| CPU Rate | 3% | 2.5% |
| RAM Rate | 65% | 65% |
| GPU UTL | 88% | 91% |
| Eval Rate(tokens/s) | 35.87 | 18.99 |
Performance Analysis for DeepSeek-r1 14b on Ollama 0.5.7
1. GPU Utilization
- A4000 Server: 88%
- P100 Server: 91%
2. Evaluation Rate
- A4000 Server: 35.87 tokens/s
- P100 Server: 18.99 tokens/s
3. RAM Utilization
4. CPU Usage
4. Download Speed
Why Choose A4000 VPS for DeepSeek-r1 14B Hosting?
- Higher Performance: The superior FP32 performance (19.2 TFLOPS) of the RTX A4000 ensures faster computation, making it ideal for hosting DeepSeek-r1 14b.
- Cost Efficiency: With competitive performance at a likely lower cost than the P100, the A4000 is more budget-friendly for startups and SMEs.
- Faster Evaluation Rate: Its 35.87 tokens/s evaluation rate is crucial for latency-sensitive use cases.
Professional GPU VPS - A4000
- 32GB RAM
- 24 CPU Cores
- 320GB SSD
- 300Mbps Unmetered Bandwidth
- Once per 2 Weeks Backup
- OS: Linux / Windows 10
- Dedicated GPU: Quadro RTX A4000
- CUDA Cores: 6,144
- Tensor Cores: 192
- GPU Memory: 16GB GDDR6
- FP32 Performance: 19.2 TFLOPS
- Available for Rendering, AI/Deep Learning, Data Science, CAD/CGI/DCC.
Professional GPU Dedicated Server - P100
- 128GB RAM
- Dual 10-Core E5-2660v2
- 120GB + 960GB SSD
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: Nvidia Tesla P100
- Microarchitecture: Pascal
- CUDA Cores: 3584
- GPU Memory: 16 GB HBM2
- FP32 Performance: 9.5 TFLOPS
- Suitable for AI, Data Modeling, High Performance Computing, etc.
Advanced GPU Dedicated Server - RTX 3060 Ti
- 128GB RAM
- Dual 12-Core E5-2697v2
- 240GB SSD + 2TB SSD
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: GeForce RTX 3060 Ti
- Microarchitecture: Ampere
- CUDA Cores: 4864
- Tensor Cores: 152
- GPU Memory: 8GB GDDR6
- FP32 Performance: 16.2 TFLOPS
Enterprise GPU Dedicated Server - RTX 4090
- 256GB RAM
- Dual 18-Core E5-2697v4
- 240GB SSD + 2TB NVMe + 8TB SATA
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: GeForce RTX 4090
- Microarchitecture: Ada Lovelace
- CUDA Cores: 16,384
- Tensor Cores: 512
- GPU Memory: 24 GB GDDR6X
- FP32 Performance: 82.6 TFLOPS
- Perfect for 3D rendering/modeling , CAD/ professional design, video editing, gaming, HPC, AI/deep learning.
When to Consider P100?
- Larger RAM Capacity: The 128GB RAM makes the P100 suitable for multitasking and memory-intensive workloads.
- Sustained GPU Performance: The HBM2 memory architecture of the Tesla P100 can maintain performance under prolonged workloads.
Conclusion
For optimal DeepSeek-r1 hosting, the GPU VPS - A4000 outperforms the P100 in key areas such as evaluation speed and overall GPU performance. The Quadro RTX A4000's CUDA and Tensor cores, combined with its impressive FP32 capabilities, make it the preferred choice for deploying DeepSeek-r1 14b on Ollama 0.5.7. On the other hand, the P100 remains a solid option for users prioritizing RAM-intensive tasks or parallel processing.
By aligning your server selection with your workload requirements, you can unlock the full potential of DeepSeek-r1 for diverse applications in AI and machine learning.
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