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NVIDIA A100 40GB vs 80 GB GPU Comparison (2024 Update)

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NVIDIA A100 40GB vs 80 GB GPU Comparison (2024 Update)

Even today, the NVIDIA A100 Tensor Core remains one of the most powerful GPUs that you can use for AI training or inference projects. While it has been overtaken in pure computational power by the H100, the A100 offers an excellent balance of raw compute, efficiency and scalability. 

While it was initially in short supply, availability of the A100 has improved over the past year and today you can get access to both versions of the A100, a 80GB and 40GB model through Cloud GPU platforms like DataCrunch.    Let’s go through what you need to know about the difference of these two models is in specs, performance and price. 

A100 40GB vs 80GB Comparison 


A100 40GB 

A100 80GB 

Memory Configuration 

40GB HBM2 

80GB HBM2e 

Memory Bandwidth 

1.6 TB/s 

2.0 TB/s 

Cuda Cores 






Tensor Cores 




54.2 billion 

54.2 billion  

Power consumption 

400 Watts 

400 Watts 

Launch date 

May 2020 

November 2020 

*See a more detailed outline of A100 specs.  

Memory Capacity 

The obvious difference between the 40GB and 80GB models of the A100 is their memory capacity. By doubling memory capacity, the 80GB model is ideal for applications requiring substantial memory, such as large-scale training and inference of deep learning models. The increased memory allows for larger batch sizes and more extensive datasets, leading to faster training times and improved model accuracy. 

Memory Bandwidth 

The memory bandwidth also sees a notable improvement in the 80GB model. With 2.0 TB/s of memory bandwidth compared to 1.6 TB/s in the 40GB model, the A100 80GB allows for faster data transfer and processing. This enhancement is important for memory-intensive applications, ensuring that the GPU can handle large volumes of data without bottlenecks. 

Common use cases for the A100 40GB 

The 40GB version of the A100 is well-suited for a wide range of AI and HPC applications. It provides plenty of memory capacity and bandwidth for most workloads, enabling efficient processing of large datasets and complex models. 

For RNN-T Inference, the performance of the 40GB and 80GB A100 were comparable. (Source: nvidia.com)  

Common use cases for the A100 80GB 

The 80GB version of the A100 doubles the memory capacity and increases the memory bandwidth to 2 TB/s. This configuration is particularly beneficial for compute-hungry AI applications that involve larger models and datasets, such as natural language processing (NLP) and scientific simulations. The additional memory capacity and bandwidth enable faster data transfer and processing, reducing training times and improving overall performance.  The increased memory capacity and bandwidth of the 80GB A100 have several performance implications: 

In a direct comparison, the A100 80GB is capable of 3x faster FP16 DLRM training than the A100 40GB (source: Nvidia.com

 Difference between the A100 PCIe and SXM 

In addition to two memory configurations, it’s important to know that the A100 comes in two form factors, the SXM4 and PCIe


A100 80GB PCIe 

A100 80GB SXM 

Memory Bandwidth 

1,935 GB/s 

2,039 GB/s 

Max Thermal Design Power 


400W (up to 500W) 

Form Factor 




NVLink Bridge for up to 2 GPUs: 600 GB/s   

NVLink: 600 GB/s 

Multi-Instance GPU (MIG) 

Up to 7 MIGs @ 5GB 

Up to 7 MIGs @ 10GB 

The SXM version provides higher memory bandwidth and a higher maximum TDP, making it suitable for more intense workloads and larger server configurations. The PCIe version is more flexible in terms of cooling options and is designed for compatibility with a wider range of server setups. 

A100 80GB vs 40GB Pricing 

For a long time the NVIDIA A100 was in extremely limited supply, so you couldn’t buy access to its compute power even if you wanted. Today, availability has improved and you can access both the A100 40GB and 80GB on-demand or reserving longer term dedicated instances. Current on-demand prices of A100 instances at DataCrunch: 

*real time A100 prices can be found here.  

Bottom line on the A100 40GB and 80GB 

Both the A100 40GB and 80GB GPUs deliver exceptional performance for AI, data analytics, and HPC. The choice between the two models should be driven by the specific memory and bandwidth requirements of your workloads. The A100 80GB model, with its substantial increase in memory capacity and bandwidth, is the go-to option for the most demanding applications.  

Now that you have a better idea of the difference between the 40GB and 80GB models of the A100, why not spin up an on-demand GPU instance with DataCrunch?