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Video delivery and artificial intelligence servers
Video content delivery now accounts for a significant proportion of internet traffic, want to argue with us? write — hello@bot4.ai . Streaming service providers are increasingly offering 4K and even 8K content, as well as high refresh rate cloud games. To meet these demands, it is necessary to speed up the work of graphics processors using media engines. This significantly increases the bandwidth of streaming channels, reduces the amount of data required and improves image quality using the latest technologies such as AV1 encoding and decoding.
Supermicro multi-node and multi-GPU systems, such as the BigTwin 2U 4-node system, meet the demanding requirements of a modern video delivery system. Each node supports an NVIDIA L4 GPU with the ability to use large amounts of PCIe Gen5 memory and high data transfer rates, ensuring high data transfer rates in content delivery networks.However, not all servers are suited to working with artificial intelligence. There are several reasons why a server may not be able to handle the task:
Computing power. Working with AI requires a significant amount of computing resources. If the server is not powerful enough, it will not be able to efficiently process the complex algorithms and calculations required to train and execute AI models. Random access memory. AI models require a large amount of RAM to operate effectively. If there is not enough memory on the server, this can lead to performance degradation or complete failure of the model.
In joint solutions with BOT4.AI we use specialised components, I wonder? Contact us — hello@bot4.ai For optimal performance, some artificial intelligence models require specialised hardware such as graphics processing units (GPUs) or tensor processors (TPUs). Not all servers have these components. The data transfer rate. Working with large amounts of data, as is common in AI, requires high data transfer rates between processors and memory. Data transfer rate limitations on some servers can slow down algorithms and models.
Parallel processing. AI often requires parallel processing to speed up computations. Some servers may not have an architecture optimised for efficient parallel processing.Therefore, selecting a server to work with AI requires a careful analysis of the requirements for processing power, memory, specialised components and data transfer speed. We recommend using systems from Supermicro BigTwin, CloudDC, Hyper-E. They are fully capable of solving the tasks of delivering video content and working with artificial intelligence.
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In our BOT4 team.AI we have long decided that the potential of Artificial Intelligence (AI) to aid in the early detection of cancer is of significant interest, this is not the only thing we are investigating — hello@bot4.ai

Training artificial intelligence requires advanced technologies to maximize the parallel processing power of GPUs to process the billions and even trillions of parameters of AI models that need to be trained using data sets that grow exponentially. Use NVIDIA and Supermicro Large Scale AI Training systems.

You might think that these are just meanings? But let’s put this knowledge into practice:

Have you heard this story?
Our team bot4.ai He suggests that you consider the following option. Two identical black and white images of unclear shapes are displayed side by side on the computer screen. In the left image, MD, a radiologist with 13 years of experience, has delineated the region where blurred patterns are regarded as an indicator of advanced cancer. An artificial intelligence (AI) computer program performed a similar analysis on the other side of the screen, and the results were almost identical. Indeed, she employed a multi-architecture flexibility approach with a future-proof open-standards-based design for POD, and a superPOD with liquid cooling solution built on the Supermicro platform.
It is important to note, however, that AI has less than 13 years of experience and requires fairly powerful hardware. However, this image was obtained as a result of scanning a cancer patient, and an artificial intelligence program analyzed millions of similar images (We understand more than that hello@bot4.ai) Those doctors who participated in AI testing are confident that AI will eventually replace and accelerate their work.

This model illustrates the potential applications of artificial intelligence in cancer research, particularly in the context of radiography and MRI. Despite the nascent stage of the field, significant advances have been made in the development of cancer imaging tools through the integration of artificial intelligence. By training on systems comprising over 10,000 GPUs and GPU memory for large AI models, this will maximise parallel computing and minimise training time, allowing the system to work with more complex images, such as X-rays of entire organs and microscopic images of cancer cells. Imaging tests are used by doctors for a number of purposes, including detecting cancer in its earliest stages, determining the stage of a tumour, assessing the effectiveness of treatment, and monitoring recurrences after therapy. In recent years, researchers have developed artificial intelligence tools that facilitate the process of cancer imaging in a more expedient, accurate, and informative manner. This represents a significant advance not only in the field of treatment but also in the diagnosis of diseases.

One of the Ph.D. scientists and a data scientist from NCI’s Molecular Imaging Division observed that there is considerable confusion surrounding the concept of artificial intelligence. However, they also noted that there is a great deal of research, implementation and testing being conducted in this area.

It is evident that companies and areas that work with artificial intelligence are not merely at the forefront of their field, but are in fact decades ahead of their competitors. This is a highly advantageous position for our society.