AI and machine learning workloads demand massive GPU power. Whether you’re training large language models, running inference at scale, or crunching big datasets, choosing the right GPU server in 2025 can make or break your project’s performance.
In this guide, we’ll compare NVIDIA A100, H100, and RTX GPUs for AI/ML workloads, with real-world considerations like cost, availability, and best use cases.
NVIDIA A100: The AI Workhorse
Launched in 2020, the A100 quickly became the industry standard for data center AI workloads.
- Memory: 40–80 GB HBM2e
- Performance: ~312 TFLOPS (FP16)
- Best for: Training mid-to-large models, distributed clusters
- Pros: Widely available, proven software ecosystem (CUDA, cuDNN, TensorRT)
- Cons: Outpaced by H100 in raw performance, but still cost-efficient
💡 2025 Outlook: Still a strong choice for colocation and private clouds where cost per TFLOP matters.
NVIDIA H100: The Current Flagship
The H100 (Hopper architecture) is NVIDIA’s most powerful AI GPU available in 2025.
- Memory: 80 GB HBM3
- Performance: ~1,000 TFLOPS (FP16)
- Best for: Cutting-edge AI training (GPT-4, LLaMA, multimodal models)
- Pros: Blazing fast, supports FP8 for higher efficiency, optimized for AI frameworks
- Cons: Expensive, limited global availability
💡 2025 Outlook: Ideal for enterprises training frontier models or startups needing the fastest time-to-market.
NVIDIA RTX (4090/5090): The Cost-Effective Alternative
While designed for gaming and workstation workloads, RTX 4090 and the new 5090 are widely used in AI labs and startups.
- Memory: 24 GB (RTX 4090), 32 GB (RTX 5090 rumored)
- Performance: 80–100 TFLOPS (FP16 equivalent)
- Best for: Fine-tuning, inference, smaller models, AI startups on budget
- Pros: Much cheaper, widely available, easy to colocate in standard servers
- Cons: Less VRAM, no enterprise features (NVLink, ECC memory, multi-GPU scaling is trickier)
💡 2025 Outlook: A strong entry point for AI startups and cost-conscious researchers.
A100 vs H100 vs RTX: Quick Comparison Table
| Feature | NVIDIA A100 | NVIDIA H100 | NVIDIA RTX 4090/5090 |
|---|---|---|---|
| Memory | 40–80 GB HBM2e | 80 GB HBM3 | 24–32 GB GDDR6X |
| FP16 Performance | ~312 TFLOPS | ~1,000 TFLOPS | ~80–100 TFLOPS |
| ECC Memory | ✅ Yes | ✅ Yes | ❌ No |
| NVLink Support | ✅ Yes | ✅ Yes | ❌ No |
| Cost (2025) | €5,000–€10,000+ | €25,000–€35,000+ | €2,000–€3,000 |
| Best Use Case | Training ML models | Frontier AI (LLMs, multimodal) | Budget AI, inference |
Which GPU Server Should You Choose in 2025?
- Choose A100 if you want proven performance, stable supply, and strong ecosystem support at a reasonable cost.
- Choose H100 if you need maximum performance and are training state-of-the-art AI/ML models.
- Choose RTX 4090/5090 if you’re a startup, researcher, or need cost-efficient inference.
👉 At WeHaveServers, we offer dedicated GPU servers with RTX 4090/5090 and colocation options for A100/H100 clusters.
Check out our GPU Servers to get started.
FAQs
Q: Can I colocate my own GPU servers in Romania/EU?
Yes — colocation is available for custom GPU servers, with power densities up to 20 kW/rack.
Q: Which is better for inference: RTX or A100?
RTX is usually enough for inference (especially fine-tuned models). A100/H100 shine for training large models.
Q: Do I need multiple GPUs for AI/ML?
Yes, most large-scale training requires multi-GPU setups (NVLink or distributed training). For inference, a single RTX 4090/5090 can often be sufficient.
