Comparing The NVIDIA A100 80GB & H100 80GB PCIe GPUs

Comparing The NVIDIA A100 80GB & H100 80GB PCIe GPUs

In the current AI industry the demand for GPUs continues to grow everyday. With the increase in price of RAM and solid state drives among other components, companies when building their AI and machine learning infrastructure will have to budget each system depending on their needs. Hence, the NVIDIA A100 and H100 GPUs are in high demand because they still sit at the intersection of performance, ecosystem maturity and real world AI workload requirements and expectations, which makes them the most practical GPUs in the market for AI deployment.

Why A100 and H100 GPUs Are Still in Large Demand

The A100 and H100 are first and foremost GPUs specifically built for AI and machine learning workloads. With large tensor core architecture optimized for deep learning plus support for different levels of quantization and precision (FP16, TF32, and FP8), these GPUs are largely designed for large-scale matrix computations. Which makes them ideal for LLMs, computer vision pipelines, scientific computing and AI infrastructures. These GPUs still largely power current AI infrastrucutures for most AI specific businesses.

CUDA Ecosystem Locks Them In with Updates

The software ecosystem that surrounds these GPUs such as PyTorch and TensorFlow allow for CUDA technology optimization. A lot of pre-trained models are benchmarked using A100s and H100s, and these GPUs with their age and support allow mature enterprise pipelines and solid performance in terms of code, instability and deployment times.

Scaling Into Full AI Infrastructure

These GPUs even though they do function standalone, they are meant to operate as clusters with technologies such as NVLink and NVSwitches and High-speed interconnects. With multiple of the same GPU type we can achieve a single large compute environment that enables distributed training, high-throughput inference, and massive parallel workloads. Hence this two GPU models are infrastructure building blocks for AI and machine learning and not just GPUs.

Market Reality: Cost, Supply and Availability

A100:

• Widely available in used and refurbished marketing
• Lower cost per unit compared to newer models that are subpar
• Strong demand from cost-conscious buyers
• There is an available Chinese market A100 called A800 that can be imported to US markets

H100:

• Limited supply
• High demand from AI-first companies as preferred GPU compared to A100 and H200
• The premium pricing even on secondary market is backed by performance

Comparison Table