Comparing NVIDIA A100 vs NVIDIA H100: Benchmarks, Use Cases, Cost and More

The NVIDIA A100 and NVIDIA H100 are two of the most powerful and popular GPUs, designed to handle the most demanding workloads in AI, machine learning and high-performance computing. From large-scale AI training to handling complex datasets, both of these GPUs offer optimal performance. However, both GPUs differ significantly in features, making them ideal for specific use cases. For example, the NVIDIA H100 is perfect for large-scale AI training while the NVIDIA A100 is recommended for cost-effective and scalable AI workload.

Our comprehensive blog below breaks down the difference between the NVIDIA A100 and the NVIDIA H100 to help you choose the perfect GPU.

NVIDIA A100 vs NVIDIA H100: Real-World Benchmarks 

We ran real-world inference benchmarks comparing the 1x NVIDIA A100 PCIe with 1x NVIDIA H100 PCIe GPU.

Benchmark Setup

• Workload Type: Inference calls using vLLM for Meta Llama 3.1 8B Model with continuous batching enabled (default settings).
• Dataset: We used the ShareGPT Vicuna unfiltered dataset, the same dataset vLLM uses in their official benchmarks.
• Benchmark Code: Our benchmarking code is based on vLLM’s benchmark suite (v0).
• Output Length: Max output length was capped at 4096 tokens.
• Other Parameters: All other parameters were left as default vLLM values.

The above benchmarks show that the NVIDIA H100 PCIe consistently outperforms the NVIDIA A100 80GB PCIe across all batch sizes when running inference using vLLM for the Llama 3.1 8B model. While the performance gain is modest at smaller batch sizes (~15%), it scales significantly with larger batches, with up to 33% faster throughput at the highest batch size tested. 

NVIDIA A100 vs NVIDIA H100 Comparison Table

NVIDIA A100 Use Cases

Check out the most popular use cases of NVIDIA A100:

1. 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 A100 scales effortlessly across multiple GPUs to handle massive datasets for enhanced model performance.
2. 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 great option for companies working with large text corpora.
3. AI Inference: The NVIDIA A100's ability to support AI inference at scale makes it well-suited for industries such as e-commerce and healthcare, where real-time decision-making based on AI predictions is essential. The GPU efficiently handles AI workloads such as predictive analytics and recommendation engines.
4. High-Performance Computing (HPC): The NVIDIA A100 shines in scientific simulations and research, so organisations to simulate complex physical processes like weather patterns, protein folding, or material science. Its high-performance computing capabilities help reduce the time and cost associated with these intensive tasks.
5. Data Analytics: Enterprises working with large volumes of data rely on the NVIDIA  A100 for advanced analytics. It accelerates complex data processing tasks like big data analytics, anomaly detection, and fraud detection by efficiently running parallel computations and handling vast data volumes in real time.

Do Check Out: 5 Real-World Use Cases of NVIDIA A100 GPUs 

NVIDIA H100 Use Cases 

Check out the most popular use cases of NVIDIA H100:

1. Large-Scale AI Model Training: Designed for ultra-high-performance AI training, the NVIDIA H100 is perfect for training next-generation AI models like large transformer models and deep neural networks. It accelerates the process, providing faster iteration cycles and enabling larger models that require massive computational power.
2. Natural Language Processing and Deep Learning: With its ability to handle heavy computational workloads, the NVIDIA H100 excels in NLP tasks such as training large language models like GPT and BERT for applications in text generation, summarisation, and multilingual translation. The NVIDIA H100's NVLink connectivity ensures efficient scaling across multiple GPUs.
3. AI Inference: For mission-critical AI inference applications in sectors like autonomous vehicles, smart cities, and robotics, the NVIDIA  H100 delivers exceptional low-latency performance. It's 600 GB/s bidirectional NVLink and high-speed networking capabilities ensure rapid AI decision-making, even in complex environments.
4. Accelerating Autonomous Systems: The NVIDIA H100's massive computational power and network optimisation features make it ideal for autonomous systems, including self-driving cars, drones, and robotics. It can process high volumes of sensor data in real-time, for machine learning models to react faster in dynamic environments.
5. High-Performance Computing (HPC) and Simulation: The NVIDIA H100 is built for next-gen simulations in fields like genomics, financial modelling, and quantum computing. Its InfiniBand and NVSwitch architecture for parallel processing, allowing faster computation of highly complex simulations that require large-scale data throughput and high computational power.

Check out 5 Interesting Facts About the NVIDIA H100 GPUs

Choosing Between NVIDIA A100 and NVIDIA H100

While both NVIDIA A100 and NVIDIA H100 are the preferred choice for AI, machine learning, and high-performance computing, the choice depends largely on the type of workload being handled:

NVIDIA A100

At Hyperstack, we offer NVIDIA A100 PCIe and NVIDIA A100 PCIe with NVLink. Our A100 GPU with NVLink offers efficient and seamless scaling across GPUs. The NVLink interconnect provides high-bandwidth communication for smooth distributed AI training and inferencing. With up to 350 Gbps of high-speed networking, the NVIDIA A100 GPU on Hyperstack enables ultra-low latency communication between nodes for your AI workloads. Hence, if you are looking for more cost-effective AI training and inference, the NVIDIA A100 remains a good choice for just $1.35/hour.

Also Read: Why Choose NVIDIA A100 PCIe for Your Workloads

NVIDIA H100

Hyperstack offers a variety of NVIDIA H100 configurations for high-end performance including PCIe, PCIe with NVLink and the SXM5.

• NVIDIA H100 PCIe and PCIe with NVLink: These NVIDIA H100 GPUs provide 350 Gbps high-speed networking, improving AI model training with minimal latency. Both configurations are designed for large-scale, performance-demanding AI workloads.
• NVIDIA H100 SXM: Our SXM version takes performance further by supporting high-speed networking of up to 350 Gbps and InfiniBand connectivity of up to 400 Gbps upcoming flavours, ideal for complex workflows. The NVIDIA H100 SXM also offers NVLink with 600 GB/s bidirectional GPU-to-GPU communication for rapid data sharing for high-performance and distributed AI workloads.

If you are looking to scale your AI projects, the NVIDIA H100 SXM delivers superior performance for large-scale AI workloads for $2.40/hour.  

Similar Reads

• Comparing NVIDIA H100 PCIe vs SXM

• NVIDIA A100 PCIe vs NVIDIA A100 SXM: A Comprehensive Comparison

• NVIDIA A6000 vs NVIDIA A100 Across Various Workloads

FAQs

What is the difference between NVIDIA A100 and NVIDIA H100?

The NVIDIA A100 is built on the Ampere architecture and offers cost-effective AI model training and inference. The NVIDIA H100, built on the Hopper architecture, delivers significantly higher performance, especially for large-scale AI training.

Which GPU is better for large-scale AI training, NVIDIA A100 or H100?

The NVIDIA H100 is better for large-scale AI training due to features like the dedicated Transformer Engine and faster memory. It offers up to 4x faster training compared to the A100.

How do the power consumptions of NVIDIA A100 and NVIDIA H100 compare?

The NVIDIA A100 consumes 300W for PCIe and 400W for SXM, while the NVIDIA H100 consumes 350W for PCIe and up to 700W for SXM. The H100 offers increased performance but requires higher energy.

What are the best use cases for the NVIDIA A100?

The A100 is ideal for cost-effective AI model training, natural language processing (NLP), AI inference, HPC tasks, and large-scale data analytics. It's a versatile choice for many AI workloads.

What are the ideal applications for the NVIDIA H100?

The NVIDIA H100 excels in large-scale AI model training, NLP tasks, autonomous systems, and HPC simulations. It provides cutting-edge performance for demanding AI applications.

How much does the NVIDIA A100 cost at Hyperstack?

At Hyperstack, the cost of NVIDIA A100 is $1.35 per hour, offering a cost-effective option for scalable AI training and inference workloads. Access NVIDIA A100 now!

What is the price of the NVIDIA H100 SXM at Hyperstack?

The NVIDIA H100 SXM at Hyperstack is available for $2.40 per hour, offering high-end performance for large-scale AI workloads. Access NVIDIA H100 SXM now!