Best Server for DeepSeek-R1:32B Reasoning | H100 vs. RTX 4090 vs. A6000
As large language models (LLMs) like DeepSeek-R1:32B become increasingly popular for AI reasoning and inference, choosing the right GPU server is crucial for achieving optimal performance at a reasonable cost. In this article, we compare four powerful GPU dedicated servers—Nvidia H100, RTX 4090, A6000, and A5000—to determine the best option for running DeepSeek-R1:32B on Ollama efficiently.
Key Factors in Choosing a GPU Server for DeepSeek-R1:32B
When selecting a GPU server for LLM inference, the following factors play a critical role:
- Performance – How fast can the server process DeepSeek-R1:32B reasoning tasks?
- GPU Utilization – Efficient use of GPU resources impacts stability and throughput.
- Memory Capacity – Large models like DeepSeek-R1:32B require high VRAM.
- Cost vs. Performance – The balance between speed and affordability is key.
- Suitability for Different Users – Some servers are better for research, while others suit commercial applications.
👉 Performance Comparison: H100 vs. RTX 4090 vs. A6000 vs. A5000
Based on benchmark tests, here’s a detailed comparison of the four servers running DeepSeek-R1:32B:
| GPU Servers | GPU Server - H100 | GPU Server - RTX4090 | GPU Server - A6000 | GPU Server - A5000 |
|---|---|---|---|---|
| Server Configs | Price: $2599.00/month GPU: Nvidia H100 Compute Capability: 9.0 Microarchitecture: Hopper CUDA Cores: 14,592 Tensor Cores: 456 GPU Memory: 80GB HBM2e FP32: 183TFLOPS Order Now > | Price: $549.00/month GPU: Nvidia GeForce RTX 4090 Compute Capability: 8.9 Microarchitecture: Ada Lovelace CUDA Cores: 16,384 Tensor Cores: 512 GPU Memory: 24GB GDDR6X FP32: 82.6 TFLOPS Order Now > | Price: $549.00/month GPU: Nvidia Quadro RTX A6000 Compute Capability: 8.6 Microarchitecture: Ampere CUDA Cores: 10,752 Tensor Cores: 336 GPU Memory: 48GB GDDR6 FP32: 38.71 TFLOPS Order Now > | Price: $349.00/month GPU: Nvidia Quadro RTX A5000 Compute Capability: 8.6 Microarchitecture: Ampere CUDA Cores: 8192 Tensor Cores: 256 GPU Memory: 24GB GDDR6 FP32: 27.8 TFLOPS Order Now > |
| Platform | Ollama0.5.7 | Ollama0.5.7 | Ollama0.5.7 | Ollama0.5.7 |
| Model | deepseek-r1:32b, 20GB, Q4 | deepseek-r1:32b, 20GB, Q4 | deepseek-r1:32b, 20GB, Q4 | deepseek-r1:32b, 20GB, Q4 |
| Downloading Speed(MB/s) | 113 | 113 | 113 | 113 |
| CPU Rate | 4% | 3% | 5% | 3% |
| RAM Rate | 3% | 3% | 4% | 6% |
| GPU vRAM | 20% | 90% | 42% | 90% |
| GPU UTL | 83% | 98% | 89% | 97% |
| Eval Rate(tokens/s) | 45.36 | 34.22 | 27.96 | 24.21 |
Screen shoots of benchmarking DeepSeek-R1:32B on Ollama
Best GPU Server for DeepSeek-R1:32B
1️⃣. Best Performance: Nvidia H100
Recommended Reason:
- Fastest eval rate (45.36 tokens/s) – 2x faster than A5000.
- 80GB VRAM – Essential for large LLMs like DeepSeek-R1:32B.
- Enterprise-grade reliability with HBM2e memory.
Best for: Large-scale AI applications, enterprise users, cloud AI services.
Downside: Very expensive ($2599/month). Only worth it for high-demand production AI.
Downside: Very expensive ($2599/month). Only worth it for high-demand production AI.
2️⃣. Best Value for Money: RTX 4090
Recommended Reason:
- Much cheaper ($549/month) vs. H100 ($2599/month).
- Decent eval rate (34.22 tokens/s) – ~75% of H100 speed at 21% of the cost.
- Best for AI startups, individual researchers, and cost-sensitive projects.
Best for: AI engineers, independent developers, research labs with a budget.
Downside: 24GB VRAM limits extreme-scale LLM tasks.
Downside: 24GB VRAM limits extreme-scale LLM tasks.
3️⃣. Best for Researchers: RTX A6000
Recommended Reason:
- 48GB VRAM – More than RTX 4090, allowing better inference stability.
- Lower utilization (89%) means less risk of overheating or throttling.
Best for: AI researchers, universities, model fine-tuning experiments.
Downside: Slightly slower (27.96 tokens/s) vs. RTX 4090 (34.22 tokens/s).
Downside: Slightly slower (27.96 tokens/s) vs. RTX 4090 (34.22 tokens/s).
4️⃣. Best Budget Option: RTX A5000
Recommended Reason:
- Most affordable ($349/month) – Great for entry-level AI experimentation.
- Still runs DeepSeek-R1:32B decently (24.21 tokens/s).
Best for: Hobbyists, small AI projects, budget-conscious researchers.
Downside: Slowest of the four, but acceptable.
Downside: Slowest of the four, but acceptable.
Final Recommendation: Which GPU Should You Choose?
- If you have unlimited budget → Choose H100 for top performance.
- If you need a balance of speed & price → Choose RTX 4090.
- If you’re a researcher needing more VRAM → Choose RTX A6000.
- If you want the cheapest option → Choose RTX A5000.
Overall Best Choice: RTX 4090 – 75% of H100 performance at a fraction of the cost.
📢 Get Started with the Best GPU Server for DeepSeek-R1:32B
Looking to rent a high-performance GPU server for DeepSeek-R1:32B? Explore Nvidia H100, RTX 4090, and A6000 options for LLM inference, AI research, and deep learning development.
Advanced GPU Dedicated Server - A5000
$ 349.00/mo
1mo3mo12mo24mo
Order Now- 128GB RAM
- Dual 12-Core E5-2697v2
- 240GB SSD + 2TB SSD
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: Nvidia Quadro RTX A5000
- Microarchitecture: Ampere
- CUDA Cores: 8192
- Tensor Cores: 256
- GPU Memory: 24GB GDDR6
- FP32 Performance: 27.8 TFLOPS
- $174.5 first month, then enjoy a 20% discount for renewals.
Enterprise GPU Dedicated Server - RTX 4090
$ 409.00/mo
1mo3mo12mo24mo
Order Now- 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.
Flash Sale to Mar.16
Enterprise GPU Dedicated Server - RTX A6000
$ 384.00/mo
30% OFF Recurring (Was $549.00)
1mo3mo12mo24mo
Order Now- 256GB RAM
- Dual 18-Core E5-2697v4
- 240GB SSD + 2TB NVMe + 8TB SATA
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: Nvidia Quadro RTX A6000
- Microarchitecture: Ampere
- CUDA Cores: 10,752
- Tensor Cores: 336
- GPU Memory: 48GB GDDR6
- FP32 Performance: 38.71 TFLOPS
- Optimally running AI, deep learning, data visualization, HPC, etc.
Flash Sale to Mar.16
Enterprise GPU Dedicated Server - H100
$ 1819.00/mo
30% OFF Recurring (Was $2599.00)
1mo3mo12mo24mo
Order Now- 256GB RAM
- Dual 18-Core E5-2697v4
- 240GB SSD + 2TB NVMe + 8TB SATA
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: Nvidia H100
- Microarchitecture: Hopper
- CUDA Cores: 14,592
- Tensor Cores: 456
- GPU Memory: 80GB HBM2e
- FP32 Performance: 183TFLOPS
Final Thoughts
The best GPU server for DeepSeek-R1:32B depends on your needs: H100 for speed, RTX 4090 for affordability, A6000 for research, and A5000 for budget users. Choose wisely based on performance, cost, and intended use case!
What GPU are you using for DeepSeek-R1:32B? Let us know in the comments! 🎯
Tags:
DeepSeek-R1:32B, AI Reasoning, Nvidia H100, RTX 4090, GPU Server, Large Language Model, AI Deployment, Deep Learning, FP32 Performance, GPU Hosting