A100 vs H100
Explore a head to head comparison of specifications, performance, and pricing.
A100
The NVIDIA A100 is a powerful Ampere-based GPU designed for AI training, inference, and high-performance computing workloads.
H100
The NVIDIA H100 is a Hopper-based GPU that provides exceptional performance, scalability, and economics for AI, deep learning, and HPC workloads.
A100 vs H100: Which Should You Choose?
The H100 offers 80 GB of VRAM — 2× the 40 GB on the A100 — making it better suited for large model workloads that require holding more parameters in GPU memory. On FP16 throughput, the H100 delivers 267.6 TFLOPS versus 77.97 TFLOPS on the A100 — 3× faster for mixed-precision training and inference. Memory bandwidth favors the H100 at 0.00 TB/s compared to 0.00 TB/s on the A100, which directly impacts inference latency for memory-bandwidth-bound models. Architecturally, the A100 is built on Ampere while the H100 uses Hopper, reflecting different generational capabilities and optimizations. On Shadeform, the A100 starts from $1.36/hr versus $1.66/hr for the H100 — 22% more expensive — reflecting the performance premium. The H100 is available across 13 cloud providers on Shadeform compared to 5 for the A100, giving more options for region and pricing flexibility.
A100 — Best Use Cases
- •General-purpose deep learning training
- •Fine-tuning models up to 13B parameters
- •AI inference at moderate throughput
- •Computer vision and NLP workloads
Choose A100 when:
- ✓40 GB VRAM is sufficient for your workload
- ✓Cost efficiency is your primary concern
- ✓Your workload does not require peak FP16 throughput
- ✓Your preferred provider already has availability
H100 — Best Use Cases
- •Training large language models (7B–405B parameters)
- •High-throughput LLM inference
- •Mixture-of-experts and transformer workloads
- •Distributed multi-GPU training runs
Choose H100 when:
- ✓You need 80 GB+ VRAM for large models or long context windows
- ✓Maximum performance justifies the higher cost
- ✓You are training large models or running high-throughput inference
- ✓You need flexibility across multiple cloud providers or regions
See how the A100 & H100 compare
Compare detailed hardware specifications and average pricing for the A100 and H100.
Compare Hardware Specifications
| A100 | H100 | |
|---|---|---|
| GPU Type | A100 | H100 |
| VRAM per GPU | 40 GB | 80 GB |
| Manufacturer | NVIDIA | NVIDIA |
| Architecture | Ampere | Hopper |
| Interconnect | PCIe Gen4 or SXM4 | PCIe Gen5 or SXM5 |
| Memory Bandwidth | 1.55 TB/s | 3.35 TB/s |
| FP16 TFLOPS | 77.97 TFLOPS (4:1) | 267.6 TFLOPS (4:1) |
| CUDA Cores | 6912 | 16896 |
| Tensor Cores | 432 (3rd Gen) | 528 (4th Gen) |
| Base Clock | 765 MHz | 1365 MHz |
| Boost Clock | 1410 MHz | 1785 MHz |
| TDP | 250W-400W | 350-700W |
| Process Node | TSMC 7nm | TSMC 4N |
| Data Formats | INT8, BF16, FP16, TF32, FP32, FP64 | FP8, INT8, BF16, FP16, TF32, FP32, FP64 |
Compare Average On-Demand Pricing
| A100 | H100 | |
|---|---|---|
| 1 GPU | $1.88 /hr | $3.03 /hr |
| 2 GPUs | $4.38 /hr | $5.61 /hr |
| 4 GPUs | $8.64 /hr | $10.46 /hr |
| 8 GPUs | $14.90 /hr | $20.15 /hr |
Frequently Asked Questions: A100 vs H100
The main differences are VRAM (40 GB vs 80 GB), FP16 throughput (77.97 vs 267.6 TFLOPS), architecture (Ampere vs Hopper). The A100 uses the Ampere architecture while the H100 is based on Hopper, giving each GPU different generational capabilities.
The H100 is generally better for large language model training due to its higher throughput and 80 GB of VRAM, which allows fitting larger models or larger batch sizes in a single pass. For smaller models or fine-tuning tasks where cost matters more, both GPUs can be effective.
On Shadeform, the A100 is available from $1.36/hr. The H100 starts from $1.66/hr. Prices vary by provider, region, and contract length. Reserved commitments can reduce hourly costs significantly compared to on-demand pricing.
The H100 has more VRAM at 80 GB, compared to 40 GB on the A100. Higher VRAM allows you to run larger models without quantization, use longer context windows, and process larger batch sizes — all of which improve throughput and reduce latency for memory-bound workloads.
Based on TFLOPS per dollar, the H100 offers better raw compute value at current Shadeform on-demand rates. However, the best choice depends on your specific workload — if you need the extra VRAM or throughput of the A100, paying the premium may be justified by faster job completion and lower total cost.
The H100 is currently available across 13 cloud providers on Shadeform's network, compared to 5 for the A100. Shadeform lets you deploy either GPU across all available providers from a single platform, so you can always find available capacity without manually checking each cloud.
Mixing different GPU types in a single training cluster is generally not recommended, as it creates performance bottlenecks where faster GPUs wait for slower ones. For best results, use a homogeneous cluster of either A100 or H100. Shadeform supports on-demand clusters of up to 64 GPUs of the same type with no commitment required.
Explore A100 & H100 Instances
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