H100 GPU vs V100 GPU — What’s the Difference?

The NVIDIA H100 GPU (Hopper architecture) delivers 5–6× faster AI training and 3.7× higher memory bandwidth (3.35 TB/s vs 900 GB/s) compared to the NVIDIA V100 GPU (Volta architecture), with 80GB HBM3 memory versus 16–32GB HBM2. The H100 has 16,896 CUDA cores vs 5,120 in the V100, making it ideal for large-scale AI/ML workloads, while the V100 suits legacy systems and moderate deep learning tasks at Cloud Storage price points that are 60–70% lower.

Key Differences Between H100 and V100 GPUs

Architecture & Generation

The H100 represents a generational leap with its transformer engine optimized for large language models, while the V100 was groundbreaking in 2017 but now serves legacy deployments.

Performance Specifications

Pricing & Cost Considerations

When evaluating Cloud Storage price alongside GPU rental costs, the V100 remains significantly more affordable:

V100 on-demand: ~$1.36/hr (1 GPU)​

H100 on-demand: ~$3.76/hr (8 GPUs; ~$0.47/GPUs/hr)​

Cyfuture V100 rental: starts at ₹9/hr ($0.43/hr)​

The H100 delivers dramatically better performance-per-dollar for large-scale AI training despite higher absolute costs. For organizations with dedicated server colocation needs, the H100's efficiency reduces total infrastructure footprint and long-term operational expenses.

Use Cases

When to Choose Each GPU

Choose H100 if:

You're training foundation models or LLMs

You need maximum memory bandwidth for data-heavy workloads

Performance is critical and budget allows

You're building new AI infrastructure

Choose V100 if:

You're running established, smaller-scale models

Budget constraints prioritize Cloud Storage price efficiency

You have legacy infrastructure investments

You need dedicated server colocation at lower costs

Conclusion

The H100 GPU outperforms the V100 by 5–8× in AI workloads thanks to Hopper architecture, 80GB HBM3 memory, and 3.35 TB/s bandwidth, but the V100 remains viable for cost-conscious organizations. When factoring in Cloud Storage price and dedicated server colocation costs, the V100 offers 60–70% lower operational expenses for moderate workloads. For next-generation AI development, the H100 is unmatched; for budget-sensitive deployments, the V100 delivers solid value from providers like Cyfuture Cloud starting at $0.43/hr.​

Follow-Up Questions

Q1: Is the H100 worth the extra cost over V100?

A: Yes, for large-scale AI/ML training, the H100's 8× Tensor performance and 80GB memory justify the cost. For smaller models or legacy systems, the V100 provides better ROI at lower Cloud Storage price points.

Q2: Can I rent H100 or V100 GPUs on-demand from Cyfuture Cloud?

A: Yes, Cyfuture Cloud offers V100 GPU servers starting at ₹9/hr ($0.43/hr). H100 availability varies by region and workload requirements; contact Cyfuture for custom dedicated server colocation quotes.​

Q3: What's the main architectural difference between H100 and V100?

A: The H100 uses Hopper architecture (5nm, 80 billion transistors) with a Transformer Engine, while the V100 uses Volta architecture (12nm, 21.1 billion transistors) with first-gen Tensor Cores.

Q4: How much faster is H100 for AI training?

A: The H100 delivers 5–6× faster AI training than the V100, with FP16 Tensor performance reaching 1,000 TFLOPS versus 125 TFLOPS.​

Q5: What memory does each GPU use?

A: H100 uses 80GB HBM3 memory with 3.35 TB/s bandwidth; V100 uses 16–32GB HBM2 with 900 GB/s bandwidth.