What are the Differences Between NVIDIA A100 and H100 GPUs?

Vishnu Subramanian

Founder @JarvisLabs.ai

H100 delivers 2-3x faster AI performance than A100 due to its Hopper architecture, fourth-gen Tensor Cores, and HBM3 memory. Choose H100 for cutting-edge performance and FP8 precision support; choose A100 when cost-efficiency matters more than raw speed.

Architecture Comparison

The architectural leap from A100 to H100 represents one of NVIDIA's most significant generational improvements:

• Compute Architecture: H100 uses the newer Hopper architecture versus A100's Ampere, bringing substantial changes in how AI workloads are processed
• Transistor Count: H100 packs 80 billion transistors compared to A100's 54 billion—a 48% increase that enables more complex operations
• Manufacturing Process: H100 is built on TSMC's 4nm process versus A100's 7nm, allowing more transistors in the same space while improving power efficiency
• Transformer Engine: H100 introduces a dedicated Transformer Engine specifically optimized for today's dominant AI model architecture

These architectural changes aren't just theoretical—they translate to real performance gains that I've observed firsthand when migrating workloads from A100 to H100 instances on our platform.

Performance Benchmarks

In real-world AI tasks, the H100 significantly outperforms the A100:

The H100's performance advantage comes from several key improvements:

• Tensor Cores: Fourth-generation in H100 vs. third-generation in A100, with up to 4x performance improvement for certain operations
• Memory Bandwidth: H100 features HBM3 memory providing nearly 2x the bandwidth (3TB/s vs 1.6TB/s)
• Specialized Instruction Set: H100 includes optimizations specifically for transformer models like Llama, BERT, and GPT variants

When we upgraded our internal development cluster from A100s to H100s, our fine-tuning jobs for 7B parameter models went from taking overnight to completing in a few hours.

Memory Considerations

Memory capacity and speed are critical factors when choosing between these GPUs:

• VRAM Capacity: Both offer up to 80GB configurations (though the H100 is also available in 141GB H200 variants)
• Memory Type: H100 uses HBM3 while A100 uses HBM2e
• Memory Bandwidth: H100 delivers ~3TB/s versus A100's ~1.6TB/s—nearly double
• DPX Instructions: H100 introduces Dynamic Programming Accelerator instructions that enhance memory efficiency for certain algorithms

For large model inference, the memory bandwidth difference is often more important than raw capacity. We've seen significantly lower latency on H100s even when both GPUs have sufficient VRAM for the model.

Cost Analysis

The performance gains of the H100 come at a premium:

• JarvisLabs Pricing: H100 SXM costs $2.99/hour versus A100 at $1.29/hour—a 132% premium
• Indian Pricing: ₹242.19/hour for H100 versus ₹104.49/hour for A100—similar ratio

Despite the higher hourly rate, the total cost calculation isn't straightforward:

• Task Completion: If H100 completes tasks in half the time or less, the overall cost may be comparable or even lower
• Developer Time: Faster iteration cycles have hidden economic benefits through improved developer productivity
• Energy Efficiency: H100 is more power-efficient per computation, reducing cooling costs in self-hosted scenarios

After extensively using both A100 and H100 GPUs, I learned that raw infrastructure cost is only one part of the equation—developer time and iteration speed often matter more.

When to Choose H100

I'd recommend the H100 when:

• You're working with transformer-based models: The dedicated Transformer Engine gives H100 a massive advantage
• You need FP8 precision: H100's native FP8 support enables efficient inference without quality loss
• Response time is critical: User-facing applications where milliseconds matter
• You're training models from scratch: The speedup in training time can be transformative for research workflows
• You need maximum GPU memory bandwidth: For memory-bound operations like large embeddings

When to Choose A100

The A100 remains an excellent choice when:

• Budget is a primary constraint: You get 80% of the capability at less than half the price
• You're running established models: Well-optimized models often don't need the cutting-edge features
• You have batch workloads: When immediate response isn't critical and jobs can run overnight
• You're optimizing for cost efficiency: More GPUs per dollar can be better than fewer, faster ones
• You have existing A100 infrastructure: Compatibility with current setups may outweigh performance gains

My Recommendation

Based on our extensive experience with both GPUs, here's my practical take:

For production inference of large models (>13B parameters), H100s are worth the premium—the latency reduction translates directly to better user experience. The difference is most noticeable with larger context windows.

For development and experimentation, A100s offer tremendous value. We still use them for most of our internal prototyping and non-time-sensitive workloads.

The sweet spot for many will be using H100s for production and customer-facing deployments, while leveraging A100s (or even A6000s at $0.79/hour) for development work.

Don't overlook optimization techniques—we've squeezed impressive performance from A100s using quantization, batching strategies, and model distillation. Sometimes clever engineering beats raw hardware power.

What specific workloads are you running? I might be able to provide more tailored guidance based on your exact use case.