In the field of artificial intelligence and deep learning, the performance of GPUs directly affects the training speed and inference efficiency of models. With the rapid development of technology, several high-performance GPUs have emerged on the market, especially NVIDIA’s flagship products. This article will compare five graphics cards based on post-2020 architectures: NVIDIA H100, A100, H200, A6000, and L40S. By taking a deep dive into the performance metrics of these GPUs, this article will explore their application scenarios for model training and inference tasks. Then helps users make informed decisions when choosing the right GPU.
Which of the mainstream GPUs are good for inference? Which ones are suitable for training?
In NVIDIA H100, A100, H200, A6000, and L40s, analyze which GPUs are more suitable for model training tasks. And analyze which GPUs are more suitable for inference tasks.
Here is a table of the key performance indicators of the NVIDIA H100、A100、H200、A6000、L40s：
| GPU model | Architecture | FP16 Performance | FP32 Performance | GPU Memory | Memory Type | Bandwidth |
| H100 | Hopper | 1,671 TFLOPS | 60 TFLOPS | 80GB | HBM3 | 3.9TB/s |
| H200 | Hopper | 1,.671 TFLOPS | 67 TFLOPS | 141 GB | HBM3e | 4.8TB/s |
| A100 | Ampere | 312 TFLOPS | 19.5 TFLOPS | 40GB/80GB | HBM2 | 2.039GB/s |
| A6000 | Ampere | 77.4 TFLOPS | 38.7 TFLOPS | 48GB | GDDR6 | 768GB/s |
| L40s | Ada Lovelace | 731 TFLOPS | 91.6 TFLOPS | 48GB | GDDR6 | 864GB/s |
This table summarizes the architecture, FP16/FP32 compute performance, memory size, memory type, and memory bandwidth of each GPU. Making it easy to compare the applicability of each GPU for different task scenarios. In terms of architecture, the newer the architecture, the better the performance, and these architectures are:
- Ampere (released in 2020)
- Ada Lovelace（released in 2022）
- Hopper（released in 2022）
When choosing a GPU for large language model (LLM) training and inference, different GPUs have their own characteristics and application scenarios. The following will analyze these GPUs, discuss their advantages and disadvantages in model training and inference tasks, and help clarify the application scenarios of different GPUs.
NVIDIA H100
Applicable Scenarios:
Model training: The H100 is designed specifically for large-scale AI training. It has super computing power, large video memory, and extremely high bandwidth, and can process massive amounts of data, which is especially suitable for training large-scale language models such as GPT and BERT. The Tensor Cores are particularly good and can greatly speed up the training process.
Inference: The H100’s performance can also easily cope with inference tasks, especially when dealing with very large models. However, due to its high energy consumption and cost, it is generally only used for inference tasks that require extremely high concurrency or real-time performance.
NVIDIA A100
Applicable Scenarios:
Model training: The A100 is the main GPU for AI training in data centers, especially in mixed-precision training. Its high memory and bandwidth make it excellent for handling large models and high-volume training tasks.
Inference: The A100’s high computing power and memory also make it ideal for inference tasks, especially when it comes to handling complex neural networks and massively concurrent requests.
NVIDIA H200
Applicable Scenarios:
Model Training: The H200 is the latest addition to the NVIDIA GPU family, the first GPU to offer 141 GB of HBM3e memory and 4.8Tbps of bandwidth, which is almost double the memory capacity and 1.4 times the bandwidth of the H100. The H200 will play a key role in edge computing and Internet of Things (IoT) applications, especially in the field of Artificial Intelligence for the Internet of Things (AIOT). Its high memory capacity.
The volume and bandwidth, as well as the superior inference speed, make it ideal for handling cutting-edge AI workloads.
Inference: The H200 has the same performance as the H100, which can easily cope with inference tasks, but due to its high energy consumption and cost, it can only be used for inference tasks when extremely high concurrency or real-time performance are required.
NVIDIA A6000
Applicable Scenarios:
Model training: The A6000 is a great choice in a workstation environment, especially if large video memory is required. Although its computing power is not as good as that of the A100 or H100, it is sufficient for the training of small and medium-sized models. Its memory can also support the training tasks of larger models.
Inference: The memory and performance of the A6000 make it ideal for inference, especially in scenarios that need to process large inputs or high-concurrency inference, providing balanced performance and memory support.
NVIDIA L40s
Applicable Scenarios:
Model training: L40s is designed for workstations and has a large increase in computing power and memory, making it suitable for the training of medium- to large-scale models, especially when a combination of strong graphics processing and AI training capabilities is required.
Inference: The powerful performance and large memory of the L40s make it ideal for high-performance inference tasks, especially complex inference tasks in workstation environments. As you can see in the chart below, while the L40s are less expensive than the A100, they outperformed the A100 by a factor of 1.2 in the tests of the Wensheng diagram model, all due to its Ada Lovelace Tensor Cores and FP8 accuracy.
Conclusion
GPUs are more recommended for model training:
The H100 and A100 are currently the best choice for training large-scale models (such as GPT-3, GPT-4, etc.), with top-of-the-line computing power, video memory, and bandwidth. The H100 surpasses the A100 in performance, but the A100 is still the workhorse in current large-scale AI training.
The A6000 can train small to medium-sized models in a workstation environment.
L40S: Delivers balanced performance with excellent FP32 and Tensor Core capabilities, but is still stronger than the H100 and A100 when it comes to model training.
GPUs are more recommended for inference:
Ideal for inference tasks, the A6000 and L40s offer powerful performance and memory to efficiently handle large model inference.
The A100 and H100 perform well in hyperscale concurrent or real-time inference tasks, but because they are relatively more expensive, they are a bit of a waste of performance if they are only used for inference scenarios.
In addition, training large models will inevitably require multiple GPUs, which is where NVIDIA’s NLink technology comes in. NVLink is typically found in high-end and data center-class GPUs, but professional cards like the L40s don’t support NVLink. Therefore, it is not suitable for training relatively complex large models, and it is only recommended to train some small models with a single card. Therefore, it is more recommended to use L40s for inference tasks.
