At present, the development of artificial intelligence in full swing has led to many technological innovations, among which large language models have become a popular research field, which not only attracts many researchers in academia, but also makes developers in the field of technology applications eager to try. However, in the development of large models, there is an important factor - that is, the difference in graphics cards can actually cause significant differences in the efficiency and effectiveness of model training.
Some people say that in the era of AI, computing power is everything, but the basis of computing power is the acceleration card. But there are also a lot of acceleration card models on the market, how to choose from the many acceleration cards with uneven quality suitable for large model reasoning "King Bomb Card" is particularly important, then we have to say RTX 4090 This acceleration card, the integration of GPU cores is amazing, the GPU core on the small chip integrates thousands of CUDA cores, as well as a large number of tensor cores and RT cores, and the computing speed can reach the level of several Teraflops (trillion floating point operations per second). It provides users with powerful computing power to speed up the training of large models.
According to official data, the 4090 accelerator card adopts the Ada architecture, compared with previous generations, the computing speed is faster, the computing power is stronger, and the 24G large video memory is carried, which effectively solves the situation of insufficient video memory. At the same time, it also has good performance in image processing.
In addition, in the training of large language models, due to the complexity of large models and the increase in the amount of data, the need for support for various software has also been put on the agenda. The 4090 accelerator card has significant advantages in this regard, it supports a wide range of software ecosystems, including CUDNN library, CUDA toolkit, and various mainstream deep learning frameworks, such as TensorFlow and PyTorch.
In the process of training large models, large language models need to process billions or even tens of billions of parameters, and require a large amount of computing resources to update and optimize the weights. In the face of this demand pain point, the high-performance computing unit and parallel processing power of the 4090 graphics card can efficiently perform these computing tasks, accelerate the convergence speed of the model, and improve the training efficiency.
The 4090 accelerator card is not only for individual users, but also for university researchers, AI-driven drug research and development enterprises. So for different users, from which channels should they get the 4090 acceleration card? There are only two ways, one is to buy and buy the local tyrant version, but the disadvantage is that it will face asset depreciation and various maintenance and management problems. The second is the economic version of the leasing 4090 accelerator card, which can find a cloud service provider to rent a GPU cloud host, which eliminates the maintenance and management problems and realizes the purpose of doing big things with little money.
However, after visiting the official websites of several mainstream cloud service providers, I found that there are very few acceleration card models to choose from. Here I would like to recommend a cloud service provider from the background of supercomputing, that is, the Beijing Super Cloud Computing Center, which has the background of the Chinese Academy of Sciences behind it, which can be described as a proper and powerful faction.
Not only that, the computing resources of the Beijing Super Cloud Computing Center are also very rich, including H800, H100, A800, A100, V100, 4090, 3090, L40S, etc., and it shows the mainstream framework environment in the preset market, which is ready to use out of the box. In addition, its GPU accelerator cards come in a variety of forms, including large-scale clusters with supercomputing architecture, cloud hosts with root privileges, and bare metal.
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.
In general, the rapid development of artificial intelligence, computing power is the foundation, good computing power not only depends on a good graphics card, but also truly realizes users from usable, easy to use to cost reduction.