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Economical profile
NVIDIA A100
Ampere · 80GB SXM
Memory
80GB HBM2e
80GB HBM2e
Bandwidth
2,039GB/s
2,039GB/s
FP32
19.5 TFLOPS
19.5 TFLOPS
TDP
400W
400W
Independently verified GPU comparison Updated July 15, 2025
NVIDIA A100 vs H100
Architecture, memory, benchmarks and real-world pricing compared straight from NVIDIA’s own datasheets, so you can decide on facts, not marketing.
Performance profile
NVIDIA H100
Hopper · 80GB SXM
Memory
80GB HBM3
80GB HBM3
Bandwidth
3,350GB/s
3,350GB/s
FP32
60 TFLOPS
60 TFLOPS
TDP
700W
700W
The honest take: H100 costs about twice as much but delivers 3.2× the FP16 throughput meaning it is usually cheaper per unit of work. For memory-bound or moderate workloads, A100 remains the smarter choice.
Quick answer
Which GPU fits your workload?
The clearest answer to six common workloads-no quiz required.
Training a large LLM
13B+ parameters, pretraining or full fine-tune
H100 Transformer Engine + FP8 keep training time reasonable at this scale – what most foundation-model teams pretrain on.
Fine-tuning a mid-size model
Under 13B parameters, LoRA/QLoRA
A100 LoRA/QLoRA tunes on sub-13B models run comfortably here the default for applied ML teams fine-tuning on a budget.
Serving inference at scale
Production API, many concurrent users
H100 FP8 and 2nd-gen MIG serve more concurrent requests per GPU at lower latency common for production copilots and chat APIs.
Computer vision / classical ML
CNNs, recommenders, tabular models
A100 Mature tooling, wide support the standard pick for medical imaging, recommenders and fraud-detection models.
HPC / scientific computing
Simulation, FP32/FP64-heavy workloads
H100 67 TFLOPS FP32 and 900GB/s NVLink meaningfully outperform A100 used for climate modeling and drug-discovery simulation.
Research, PoC or tight budget
Dev/test, early-stage projects
A100 Same 80GB memory ceiling as H100 at roughly half the cost the default for university labs and early-stage startups.
Side by side
Full technical specifications
Every figure below is sourced from NVIDIA’s official datasheets.
NVIDIA A100 and NVIDIA H100 technical specification comparison
| Specification | A100 | H100 |
|---|---|---|
| Architecture | Ampere | Hopper |
| GPU memory | 40GB / 80GB HBM2e | 80GB HBM3 |
| FP32 performance | 19.5 TFLOPS | 60 TFLOPS |
| FP16 Tensor performance | 312 TFLOPS | 989 TFLOPS |
| FP64 performance | 9.7 TFLOPS | 34 TFLOPS |
| Tensor Cores | 3rd gen | 4th gen |
| Transformer Engine | No | Yes, 1st gen |
| Form factor | SXM4 / PCIe | SXM5 / PCIe |
| TDP | 400W (SXM) | 700W (SXM) |
Scaling to the H100 doubles cost but can finish heavily parallel workloads about 3× faster-so the effective cost per completed job may be lower.
Straight comparison
How much faster is H100, really?
Raw specifications translated into simple relative performance. Longer bars indicate higher output.
FP16 tensor throughput, with sparsity
TFLOPS A100
1.0×
H100
3.2×
Memory bandwidth
GB/S A100
1.0×
H100
1.64×
Memory capacity
GB A100
1.0×
H100
1.0×
FP32 tensor throughput
TFLOPS A100
1.0×
H100
3.1×
Theoretical peak performance based on NVIDIA specifications. Real-world results vary with model architecture, framework optimization level and batch size.
What you pay
Rental pricing, in the open
Real hourly pricing-no contact form and no hidden commitment.
NVIDIA A100 80GB
1× A100 in a dedicated cloud instance
₹
90,000
/mo
90,000
/mo
From ₹123/hour with no long-term contract
₹20,000 free credits · Spin up in <15 min
NVIDIA H100 HGX 80GB
1× H100 in a dedicated cloud instance
₹
180,000
/mo
180,000
/mo
Also available hourly with no lock-in
₹20,000 free credits · Help with setup
Scaling to the H100 doubles cost but can finish heavily parallel workloads about 3× faster-so the effective cost per completed job may be lower.
20,000+ businesses running on AceCloud
<15 min typical GPU provisioning
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24/7 human GPU cloud support
Ready when you are
Get matched with the right GPU, plus ₹20,000 in free credits
Tell us your workload and our GPU specialists will recommend the most cost-effective configuration usually within five minutes.
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A100, H100, H200 and RTX GPUs ready to deploy
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