NVIDIA H200 VS NVIDIA A10
Choosing between **H200** and **A10** depends on your specific AI workload requirements. The **H200** leads in both memory capacity and raw compute power, making it a stronger choice for high-end LLM training. Currently, you can rent these GPUs starting from **$1.49/h** and **$0.40/h** respectively across 45 providers.
📊 Detailed Specifications Comparison
| Specification | H200 | A10 | Difference |
|---|---|---|---|
| Architecture & Design | |||
| Architecture | Hopper | Ampere | - |
| Process Node | 4nm | 8nm | - |
| Target Market | datacenter | datacenter | - |
| Form Factor | SXM5 | Single-slot PCIe | - |
| Memory & Bandwidth | |||
| VRAM Capacity | 141GB | 24GB | +488% |
| Memory Type | HBM3e | GDDR6 | - |
| Memory Bandwidth | 4.8 TB/s | 600 GB/s | +700% |
| Memory Bus Width | 6144-bit | 384-bit | - |
| Compute Infrastructure | |||
| CUDA Cores | 16,896 | 9,216 | +83% |
| Tensor Cores (AI) | 528 | 288 | +83% |
| RT Cores (Ray Tracing) | N/A | 72 | |
| AI & Compute Performance (TFLOPS) | |||
| FP32 (Single Precision) | 67 TFLOPS | 31.2 TFLOPS | +115% |
| FP16 (Half Precision) | 1,979 TFLOPS | 62.4 TFLOPS | +3071% |
| TF32 (Tensor Float) | 989 TFLOPS | N/A | |
| FP64 (Double Precision) | 34 TFLOPS | N/A | |
| INT8 (Integer Precision) | 3,958 TOPS | N/A | |
| Power & Efficiency | |||
| TDP (Thermal Design Power) | 700W | 150W | +367% |
| PCIe Interface | PCIe 5.0 x16 | PCIe 4.0 x16 | - |
| Multi-GPU Interconnect | NVLink 4.0 (900 GB/s) | None | - |
🎯 Use Case Recommendations
LLM & Large Model Training
NVIDIA H200
Higher VRAM capacity and memory bandwidth are critical for training large language models. The H200 offers 141GB compared to 24GB.
AI Inference
NVIDIA H200
For inference workloads, performance per watt matters most. Consider the balance between FP16/INT8 throughput and power consumption.
Budget-Conscious Choice
NVIDIA A10
Based on current cloud pricing, the A10 starts at a lower hourly rate.
Technical Deep Dive: H200 vs A10
This is a generational comparison within the NVIDIA ecosystem, pitting Hopper against Ampere. The H200 has a significant **117GB VRAM advantage**, which is crucial for training massive datasets or large language models. From a cost perspective, the **A10** is currently about **73% cheaper** per hour, offering better value for budget-conscious projects.
NVIDIA H200 is Best For:
- LLM inference at scale
- Large context window models
- Budget deployments
NVIDIA A10 is Best For:
- AI inference
- Cloud gaming
- Heavy LLM training
Frequently Asked Questions
Which GPU is better for AI training: H200 or A10?
For AI training, the key factors are VRAM size, memory bandwidth, and tensor core performance. The H200 offers 141GB of HBM3e memory with 4.8 TB/s bandwidth, while the A10 provides 24GB of GDDR6 with 600 GB/s bandwidth. For larger models, the H200's higher VRAM capacity gives it an advantage.
What is the price difference between H200 and A10 in the cloud?
Cloud GPU rental prices vary by provider and region. Based on our data, H200 starts at $1.49/hour while A10 starts at $0.40/hour. This represents a 272% price difference.
Can I use A10 instead of H200 for my workload?
It depends on your specific requirements. If your model fits within 24GB of VRAM and you don't need the additional throughput of the H200, the A10 can be a cost-effective alternative. However, for workloads requiring maximum memory capacity or multi-GPU scaling, the H200's NVLink support (NVLink 4.0 (900 GB/s)) may be essential.
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