As we all know, in the field of artificial intelligence, especially in the model training and inference phases, the performance of graphics cards is crucial. As the size of the model grows, so does the need for computing power. Therefore, how to choose the right graphics card, whether you can have both fish (performance) and cost performance (bear's paw) is a topic that many model developers are very concerned about.
There are many models of acceleration cards on the market now, but when it comes to graphics cards suitable for large model inference, the 4090 graphics card is definitely the existence of "inference king card" at this stage. In terms of performance is not as good as H100, and on price is not as good as 3090, why can the seemingly mediocre 4090 graphics card stand out among many competitors and become the best choice for large model inference?
The 4090 graphics card is based on the Ada Lovelace architecture, which significantly improves computing performance, and has a large number of CUDA cores, high-speed video memory, and more advanced cooling technology. This makes the 4090 graphics card excellent for large-scale matrix operations and parallel processing, making it ideal for inference tasks for deep learning models.
Deep learning models, especially large models, require a large amount of memory to store model parameters and intermediate calculation results. In order to ensure the smooth entry and exit of these parameters and calculation results and ensure the smoothness of the entire inference process, the 4090 graphics card is equipped with at least 24GB of GDDR6X video memory, which reduces the performance bottleneck caused by insufficient video memory.
In addition, the 4090 graphics card enjoys good software ecosystem support, including CUDA toolkit, cuDNN library, and other deep learning frameworks such as TensorFlow, PyTorch, etc. Enabling the 4090 graphics card to reach its full potential not only makes it easy to migrate inference tasks, but also accelerates the inference process with its powerful computing power and optimized framework.
Although the 4090 graphics card has strong performance, large capacity, good software ecological support and flexible resource allocation capabilities, it does not "hold on to the pampered" and start the price. Compared with other GPU graphics cards, the 4090 has a very competitive price/performance ratio.
Not only does it have an excellent price-performance ratio, but its stability and reliability are also eye-catching, and it can maintain continuous stability in long-term operation and show excellent performance. This stability is a solid rock that provides solid hardware support for the inference process, ensuring that the 4090 GPU can consistently provide reliable support for inference tasks without any glitches or performance fluctuations.
Although the 4090 graphics card is known as the "king of reasoning", the demand for computing power for large model inference is huge, and it bears huge cost pressure for both enterprises and individual teams, so the mainstream method in the market is still the leasing model. At present, the domestic 4090 graphics card leasing market is dominated by two forms: "cloud host" and "GPU cluster", both of which have their own advantages and disadvantages.
Virtual machine mode: The virtual machine platform allows users to customize the configuration of GPU virtual machines according to specific computing needs, providing a variety of configuration options. The platform is easy to use, easy to operate, easy to manage, and provides users with an autonomous and controllable environment to ensure the security of user data.
GPU cluster mode: The GPU cluster platform is built on a high-performance computing (HPC) environment and supports cross-node and multi-card parallel computing. The platform provides GPU computing power and services to universities, scientific research institutions and enterprise users. Users can flexibly rent GPU resources and pay for them on a pay-as-you-go basis without bearing construction and O&M costs, allowing them to focus more on AI research.
This depends on the specific needs of the user to judge, the two modes have their own advantages and disadvantages, the cloud host use model is more inclined to ordinary computers, from the operation, the difficulty of starting is very simple, but the disadvantages of cloud host compared with the cluster mode are also very obvious, cloud host will be billed when the main boot. On the other hand, the cluster mode is more flexible and only charges for the time and number of GPUs actually consumed during the computing process. After the compute task is completed, the billing stops to ensure that the user only pays the actual compute fee. In addition, the cluster mode uses shared network bandwidth, and does not charge tenants separately for network fees, which reduces the cost of users, and does not incur any costs in the process of installing software. However, the cluster mode also has its disadvantages, that is, the Linux system needs to complete the relevant tasks in the form of a command set, which is not very friendly to users without computer foundation.
Finally, we recommend an easy-to-use computing power rental platform, which provides rich high-performance GPU computing resources, including 4090, H800, A800, A100, V100, 3090, L40S, etc., and presets the mainstream framework environment in the market, with strong performance and out-of-the-box use. New users can also receive 500 yuan card computing resources for free.