Table of Contents
Overview
The NVIDIA A100 and H100 are the two most important GPUs in the AI industry today. Released in 2020 and 2022 respectively, they represent different generations of NVIDIA's datacenter GPU technology.
NVIDIA A100
Ampere Architecture
- Released May 2020
- 40GB or 80GB HBM2e
- Widely available
- Lower cost per GPU
NVIDIA H100
Hopper Architecture
- Released March 2022
- 80GB HBM3
- 3x faster for AI training
- FP8 precision support
TL;DR - Quick Recommendation
Choose H100 if you need maximum performance for LLM training and have the budget. Choose A100 if you want better price-performance ratio or need wider availability.
Technical Specifications
Here's a detailed comparison of the technical specifications between the A100 and H100:
| Specification | A100 | H100 |
|---|---|---|
| Architecture | Ampere (GA100) | Hopper (GH100) |
| Process Node | 7nm TSMC | 4nm TSMC |
| Transistors | 54 billion | 80 billion |
| CUDA Cores | 6,912 | 16,896 |
| Tensor Cores | 432 (3rd gen) | 528 (4th gen) |
| Memory | 40GB / 80GB HBM2e | 80GB HBM3 |
| Memory Bandwidth | 2 TB/s | 3.35 TB/s |
| TDP | 400W | 700W |
| NVLink | 600 GB/s | 900 GB/s |
| PCIe | Gen 4 | Gen 5 |
| FP64 (Double) | 9.7 TFLOPS | 34 TFLOPS |
| FP32 (Single) | 19.5 TFLOPS | 67 TFLOPS |
| FP16 Tensor | 312 TFLOPS | 1,979 TFLOPS |
| INT8 Tensor | 624 TOPS | 3,958 TOPS |
| FP8 Tensor | Not supported | 3,958 TFLOPS |
| Release Date | May 2020 | March 2022 |
Key Architectural Differences
Transformer Engine
H100 includes dedicated hardware for transformer models, enabling dynamic FP8/FP16 precision switching.
HBM3 Memory
H100 uses faster HBM3 memory with 67% higher bandwidth than A100's HBM2e.
4th Gen Tensor Cores
H100's tensor cores are 2-3x faster and support new FP8 precision format.
NVLink 4.0
H100 supports 900 GB/s NVLink bandwidth, 50% more than A100.
Performance Benchmarks
Real-world performance comparisons for common AI workloads. The H100 consistently outperforms the A100, with the gap widening for transformer-based models:
GPT-3 Training
LLaMA Inference
Stable Diffusion
BERT Fine-tuning
ResNet-50 Training
Transformer Inference
Performance Summary
On average, the H100 delivers 2-4x better performance than the A100 for AI training and inference. The gap is largest for transformer models thanks to the dedicated Transformer Engine and FP8 support.
Memory & Bandwidth
Memory is crucial for training large models. Both GPUs offer 80GB variants, but the H100's HBM3 memory provides significantly higher bandwidth:
A100 Memory
- Capacity40GB / 80GB
- TypeHBM2e
- Bandwidth2 TB/s
- ECCYes
H100 Memory
- Capacity80GB
- TypeHBM3
- Bandwidth3.35 TB/s (+67%)
- ECCYes
The 67% bandwidth increase in H100 is crucial for:
- Loading large model weights faster
- Reducing memory bottlenecks during training
- Better performance for memory-bound operations
- Faster gradient synchronization in multi-GPU setups
Pricing Comparison
Cloud GPU pricing varies significantly by provider. Here's a comparison of hourly rates for single GPU instances:
| Provider | A100 Price | H100 Price |
|---|---|---|
| AWS (p4d/p5) | $32.77/hr (p4d.24xlarge) | $98.32/hr (p5.48xlarge) |
| Google Cloud | $2.93/hr (a2-highgpu-1g) | $~10/hr (a3-highgpu) |
| Azure | $3.67/hr (NC A100 v4) | $~12/hr (ND H100 v5) |
| Lambda Labs | $1.10/hr | $2.49/hr |
| Griddly Best Value | $0.80/hr | $1.99/hr |
Price-Performance Analysis
While H100 costs 2-3x more per hour, it delivers 2-4x better performance. This means:
A100 Value Proposition
- • Lower hourly cost
- • Better for budget-constrained projects
- • Good for smaller models
- • Wider availability
H100 Value Proposition
- • Similar or better cost per result
- • Faster time-to-completion
- • Essential for large LLMs
- • FP8 enables further savings
Best Use Cases
Best for A100
- Small to medium model training
- Inference workloads
- Budget-conscious projects
- Scientific computing (FP64)
- When availability is critical
- Multi-tenant environments
Best for H100
- Large language model training
- Transformer-based architectures
- Time-critical projects
- Production LLM inference
- Multi-modal AI (text + image)
- Cutting-edge research
Availability
GPU availability remains a significant factor in 2025:
A100 Availability
Generally available across major cloud providers. Lead times are typically hours to days. Spot instances often available at significant discounts.
H100 Availability
Still constrained in 2025. Major cloud providers have waitlists. Reserved capacity often requires long-term commitments. Improving but limited.
Griddly Advantage
Griddly's distributed network provides access to both A100 and H100 GPUs without the typical cloud provider waitlists. Our decentralized approach means better availability and lower prices.
Which Should You Choose?
The choice between A100 and H100 depends on your specific requirements:
Choose A100 if:
- • You're working with models under 30B parameters
- • Budget is a primary concern
- • You need immediate availability
- • Your workload is inference-heavy
- • You need FP64 for scientific computing
Choose H100 if:
- • You're training large language models (30B+ parameters)
- • Time-to-completion is critical
- • You can leverage FP8 precision
- • You're building production LLM services
- • You need the absolute best performance