TABLE OF CONTENTS
NVIDIA H100 SXM On-Demand
Key Takeaways
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NVIDIA H100 offers superior performance and memory bandwidth compared to A100, ideal for large-scale AI training and high-throughput inference.
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NVIDIA A100 remains cost-effective for scalable AI workloads, NLP tasks, and high-performance computing on moderate budgets.
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NVIDIA H100 SXM maximises multi-GPU setups with NVLink, while NVIDIA H100 PCIe provides flexibility for standard servers.
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NVIDIA A100 supports NVLink scaling for multi-node training, making it suitable for distributed AI workloads.
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The choice between NVIDIA A100 and NVIDIA H100 depends on project scale, budget, and GPU memory and interconnect requirements.
The NVIDIA H100 (Hopper) is the successor to the NVIDIA A100 (Ampere). It adds FP8 support through the Transformer Engine, faster memory and faster NVLink, and is typically 2 to 3x faster for AI training depending on precision and workload. The NVIDIA A100 remains the lower-cost option on Hyperstack, from $1.35/hour against $2.50/hour for the NVIDIA H100 PCIe.
Our guide compares the NVIDIA A100 and NVIDIA H100 across performance, target use cases, cost and efficiency on real workloads. We show where the NVIDIA A100 still delivers value and where the NVIDIA H100 accelerates next-generation AI training and inference, with Hyperstack pricing so you can match the GPU to your project goals and budget.
NVIDIA A100 vs NVIDIA H100 Comparison Table
|
Feature |
NVIDIA A100 PCIe |
NVIDIA A100 SXM |
NVIDIA H100 PCIe |
NVIDIA H100 SXM |
|---|---|---|---|---|
|
Architecture |
Ampere |
Ampere |
Hopper |
Hopper |
|
CUDA Cores |
6,912 |
6,912 |
14,592 |
16,896 |
|
Memory |
80 GB HBM2e |
80 GB HBM2e |
80 GB HBM2e |
80 GB HBM3 |
|
Memory Bandwidth |
1,935 GB/s |
2,039 GB/s |
2.0 TB/s |
3.35 TB/s |
|
BF16 Tensor Core (dense / sparse) |
312 / 624 TFLOPS |
312 / 624 TFLOPS |
756 / 1,513 TFLOPS |
989 / 1,979 TFLOPS |
|
FP8 Support |
No |
No |
Yes |
Yes |
|
Interconnect |
NVLink 3.0 bridge: 600 GB/s; PCIe Gen4: 64 GB/s |
NVLink 3.0: 600 GB/s; PCIe Gen4: 64 GB/s |
NVLink 4.0 bridge: 600 GB/s; PCIe Gen5: 128 GB/s |
NVLink 4.0: 900 GB/s; PCIe Gen5: 128 GB/s |
|
TDP |
300 W |
400 W |
300 to 350 W |
Up to 700 W |
|
MIG |
Up to 7 instances @ 10 GB |
Up to 7 instances @ 10 GB |
Up to 7 instances @ 10 GB |
Up to 7 instances @ 10 GB |
|
Cost (On-Demand) |
$1.35/hr for NVIDIA A100 PCIe; $1.40/hr for NVIDIA A100 PCIe with NVLink |
$1.60/hr |
$2.50/hr for NVIDIA H100 PCIe; $2.60/hr for NVIDIA H100 PCIe with NVLink |
$3.20/hr |
NVIDIA A100 Use Cases
Check out the most popular use cases of NVIDIA A100:
- AI Model Training and Fine-Tuning: The NVIDIA A100 is ideal for training deep learning models, including neural networks for computer vision, speech recognition and language processing. With NVLink support, the NVIDIA A100 scales across multiple GPUs to handle large datasets.
- Natural Language Processing (NLP): The NVIDIA A100 accelerates large-scale NLP tasks, such as training language models for translation, sentiment analysis and chatbots. Its architecture balances performance and cost-effectiveness, making it a strong option for companies working with large text corpora.
- AI Inference: The NVIDIA A100 supports AI inference at scale, making it well-suited for industries such as e-commerce and healthcare, where real-time decisions based on AI predictions are essential. It efficiently handles workloads such as predictive analytics and recommendation engines.
- High-Performance Computing (HPC): The NVIDIA A100 performs well in scientific simulations and research, allowing organisations to simulate complex physical processes like weather patterns, protein folding or material science. Its HPC capabilities help reduce the time and cost of these intensive tasks.
- Data Analytics: Enterprises working with large volumes of data rely on the NVIDIA A100 for advanced analytics. It accelerates big data analytics, anomaly detection and fraud detection by running parallel computations across large data volumes in real time.
NVIDIA H100 Use Cases
Check out the most popular use cases of NVIDIA H100:
- Large-Scale AI Model Training: Designed for high-performance AI training, the NVIDIA H100 suits next-generation models like large transformer models and deep neural networks. Its Transformer Engine and FP8 support shorten iteration cycles and make larger models practical.
- Natural Language Processing and Deep Learning: The NVIDIA H100 excels in NLP tasks such as training large language models like GPT and BERT for text generation, summarisation and multilingual translation. NVLink connectivity enables efficient scaling across multiple GPUs.
- AI Inference: For latency-sensitive inference in sectors like autonomous vehicles, smart cities and robotics, the NVIDIA H100 delivers low-latency performance. The NVIDIA H100 SXM's 900 GB/s NVLink and high-speed networking support fast AI decision-making in complex environments.
- Accelerating Autonomous Systems: The NVIDIA H100's compute power and network optimisation features suit autonomous systems, including self-driving cars, drones and robotics. It can process high volumes of sensor data in real time, so machine learning models react faster in dynamic environments.
- High-Performance Computing (HPC) and Simulation: The NVIDIA H100 is built for next-generation simulations in fields like genomics, financial modelling and quantum computing. Its InfiniBand and NVSwitch support enable parallel processing for complex simulations that need high data throughput.
Choosing Between NVIDIA A100 and NVIDIA H100
Both the NVIDIA A100 and NVIDIA H100 are proven choices for AI, machine learning and high-performance computing. The right choice depends on the workload:
NVIDIA A100
At Hyperstack, we offer NVIDIA A100 PCIe, NVIDIA A100 PCIe with NVLink and NVIDIA A100 SXM. The NVLink interconnect provides 600 GB/s of GPU-to-GPU bandwidth for smooth distributed AI training and inference. With up to 350 Gbps of high-speed networking, the NVIDIA A100 on Hyperstack enables low-latency communication between nodes. If you are looking for cost-effective AI training and inference, the NVIDIA A100 remains a strong choice at $1.35/hour.
NVIDIA H100
Hyperstack offers a range of NVIDIA H100 configurations for high-end performance, including PCIe, PCIe with NVLink and SXM.
- NVIDIA H100 PCIe and PCIe with NVLink: These configurations provide 350 Gbps high-speed networking, improving AI model training with minimal latency. Both are designed for large-scale, performance-demanding AI workloads, from $2.50/hour.
- NVIDIA H100 SXM: The SXM version takes performance further with high-speed networking of up to 350 Gbps and InfiniBand connectivity of up to 400 Gbps, ideal for complex workflows. It also offers NVLink 4.0 with 900 GB/s of GPU-to-GPU bandwidth for rapid data sharing in distributed AI workloads.
If you are looking to scale your AI projects, the NVIDIA H100 SXM delivers the highest performance of the four for large-scale AI workloads at $3.20/hour.
Ready to deploy NVIDIA A100 or NVIDIA H100?
Run NVIDIA A100 from $1.35/hour or NVIDIA H100 from $2.50/hour on Hyperstack, with per-minute billing and no data transfer fees. Choose on-demand GPU VMs for flexibility, or reserve capacity for long-running training.
FAQs
What is the difference between NVIDIA A100 and NVIDIA H100?
The NVIDIA H100 uses the newer Hopper architecture, with more CUDA cores, faster memory (up to 3.35 TB/s on SXM against 2,039 GB/s on the NVIDIA A100 SXM), NVLink 4.0 at 900 GB/s and FP8 support through the Transformer Engine. The NVIDIA A100 uses Ampere and does not support FP8.
How much faster is the NVIDIA H100 than the NVIDIA A100?
The NVIDIA H100 is typically 2 to 3x faster than the NVIDIA A100 for AI training, depending on precision and workload. The largest gains come from FP8 training of large transformer models.
Which is better for LLM training, NVIDIA A100 or NVIDIA H100?
The NVIDIA H100 is better for large-scale LLM training, thanks to the Transformer Engine, FP8 support and higher memory bandwidth. The NVIDIA A100 suits smaller models and fine-tuning where cost matters more than speed.
What is the Transformer Engine?
The Transformer Engine is a feature of the NVIDIA H100 that switches between FP8 and 16-bit precision layer by layer during training and inference. FP8 halves the memory and bandwidth needed per value compared with 16-bit formats, which speeds up transformer models while keeping accuracy. The NVIDIA A100 does not have a Transformer Engine.
Is the NVIDIA A100 still worth it in 2026?
Yes, for cost-sensitive work. The NVIDIA A100 handles fine-tuning, inference, smaller model training and data analytics well, and costs from $1.35/hour on Hyperstack. Choose the NVIDIA H100 when you need FP8 or faster large-scale training.
Is the NVIDIA H100 worth it over the NVIDIA A100?
The NVIDIA H100 is worth it when training time is the bottleneck. If it finishes a job 2 to 3x faster at $2.50 to $3.20/hour, the total cost per run can match or beat the NVIDIA A100.
How much does an NVIDIA H100 cost per hour?
On Hyperstack, the NVIDIA H100 costs $2.50/hour for PCIe, $2.60/hour for PCIe with NVLink and $3.20/hour for SXM.
How much does an NVIDIA A100 cost per hour?
On Hyperstack, the NVIDIA A100 costs $1.35/hour for PCIe, $1.40/hour for PCIe with NVLink and $1.60/hour for SXM.
What are the best use cases for the NVIDIA A100?
The NVIDIA A100 is ideal for cost-effective AI model training, NLP, AI inference, HPC tasks and large-scale data analytics.
What are the ideal applications for the NVIDIA H100?
The NVIDIA H100 excels in large-scale AI model training, LLM workloads, autonomous systems and HPC simulations.
Which is better for inference, NVIDIA A100 or NVIDIA H100?
The NVIDIA H100 delivers lower latency and higher throughput, especially with FP8. The NVIDIA A100 is the more economical choice for smaller models or moderate traffic.
Should I choose NVIDIA H100 PCIe or NVIDIA H100 SXM?
Choose the NVIDIA H100 SXM for multi-GPU training, as it offers 3.35 TB/s memory bandwidth and 900 GB/s NVLink. The NVIDIA H100 PCIe suits single-GPU or smaller workloads at a lower price. See Comparing NVIDIA H100 PCIe vs SXM.
Does Hyperstack offer NVIDIA A100 40GB or 80GB?
Hyperstack offers the NVIDIA A100 with 80 GB of memory across all configurations.
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