Expanding Data Universe: The Future of Artificial Intelligence and Server Architectures

Expanding Data Universe

Today, artificial intelligence has become an important part of our lives and continues to integrate more into our daily lives by increasing its influence day by day.

Undoubtedly, artificial intelligence technology facilitates users' work in many areas and accelerates processes. However, these conveniences come at a cost.

In the age of data, the importance of data is increasing day by day, and investments in the technologies required to process this data are rapidly growing. One of the fundamental building blocks of artificial intelligence technology, datasets, require powerful hardware to operate stably and efficiently.

At this point, GPUs (graphics processing units), which can be described as the 'heart' of artificial intelligence, play a critical role in meeting the demands for computational power and performance. While traditional CPUs (central processing units) are insufficient to meet these high demands, investors are increasingly turning to GPU-supported systems. Consequently, investments in GPU-focused infrastructures are accelerating and gaining priority to meet the requirements of modern artificial intelligence applications.

Ready Datasets and Llama Models

One of the current and popular ready datasets well-known by those interested in artificial intelligence is the Llama models offered by Meta (Facebook). These models provide solutions tailored to various needs with different levels of data capacity and hardware requirements. Here are the prominent versions and details of the Llama models:

Llama 3.2

1B

• Data Volume: Contains 1 billion data points.
• Features: Supports multiple languages. Suitable for personal computers and small-scale servers. Performs simple tasks easily.
• Hardware Requirements:
  • Requires a minimum of 2 GB RAM and 2 GB GPU.
  • Can operate stably using only a CPU without a GPU.

3B

• Data Volume: Contains 3 billion data points.
• Features: Offers multi-language support. Ideal for medium-scale servers and supports more complex operations.
• Hardware Requirements:
  • Requires a minimum of 4 GB RAM and 4 GB GPU.
  • Can run without a GPU, but performance significantly decreases and operates slowly.

Llama 3.2-Vision

11B

• Data Volume: Contains 11 billion data points.
• Features: Offers multi-language support and image interpretation capabilities. This model is intended for entry-level visual data processing needs.
• Hardware Requirements:
  • Requires a minimum of 20 GB RAM and 8 GB GPU.
  • When run only on a CPU, the response time to a query can be as long as 5 minutes.

90B

• Data Volume: Contains 90 billion data points.
• Features: A current, mid-level advanced model with versatile capabilities and broad application areas.
• Hardware Requirements:
  • Requires a minimum of 128 GB RAM and 141 GB GPU memory.

The Future of Server Architectures in the Data Age

As the amount of data in datasets increases, RAM and GPU requirements rise proportionally. To meet these needs, Nvidia has developed high-performance graphics cards.

Some of these powerful graphics cards support virtualization. This means, for example, a 141 GB graphics card can be divided into parts on a server using a virtualization platform like ESXi, and distributed to multiple customers in separate usage slices. This allows for more efficient use of resources and the provision of scalable solutions.

Day by day, investments made by server companies in this field are growing rapidly. In the data age, the demand for powerful GPU-supported servers is increasing, especially in areas like artificial intelligence, machine learning, and big data analytics. This encourages server companies to move away from traditional server architectures and towards GPU-based, high-performance systems.

Virtualization and GPU partitioning technologies provide flexible and cost-effective solutions for businesses while enabling faster and scalable responses to customer demands. Therefore, investments made by server companies in such technologies provide a competitive advantage and accelerate innovation in the sector.

NVIDIA A100 Tensor Core GPU

• Features:
  • Built on the Ampere architecture.
  • 54 billion transistors.
  • 6,912 CUDA cores.
  • 40 GB or 80 GB HBM2 memory options.

NVIDIA H100 Tensor Core GPU

• Features:
  • Latest technology with Hopper architecture.
  • 80 billion transistors.
  • 7,680 CUDA cores.
  • 80 GB HBM3 memory.

NVIDIA A40 GPU

• Features:
  • Ampere architecture.
  • 4,608 CUDA cores.
  • 48 GB GDDR6 memory.

NVIDIA A30 Tensor Core GPU

• Features:
  • Ampere architecture.
  • 3,840 CUDA cores.
  • 24 GB HBM2 memory.

NVIDIA RTX A6000

• Features:
  • Ampere architecture.
  • 10,752 CUDA cores.
  • 48 GB GDDR6 memory.

NVIDIA Jetson Series

• Models:
  • Jetson AGX Xavier
  • Jetson Xavier NX
  • Jetson Nano

NVIDIA DGX Systems

• Models:
  • DGX A100
  • DGX Station
• Features:
  • Includes multiple NVIDIA A100 or H100 GPUs.
  • Designed for high-performance computing and AI training.
  • Offers integrated software and hardware solutions.
• Applications:
  • Enterprise AI research.
  • Large-scale machine learning projects.
  • Data science and analytics.

NVIDIA Titan Series

• Models:
  • Titan RTX
  • Titan V
• Features:
  • High-performance CUDA cores.
  • Large memory capacity.
  • Focused on deep learning and research.
• Applications:
  • Research and development.
  • AI and deep learning projects.
  • Applications requiring high-performance computing.