A GPU (Graphics Processing Unit) is a processor that specializes in graphics processing such as images and videos. In recent years, it has been used not only for graphics processing, but also for applications such as calculations that require a large amount of parallel processing, artificial intelligence (AI) fields, and scientific and technological calculations.
The relationship between GPUs and graphics cards
GPU (Graphics Processing Unit) refers to the processor itself that specializes in graphics and parallel processing. On the other hand, a graphics card (or graphics board) refers to an expansion card with a GPU.
The main role of GPUs
- Graphics Processing
- Render (display) 3D models and draw 2D images.
- He is mainly active in games, CG production, and video editing software.
- parallel processing
- Compared to CPUs, GPUs have a large number of cores (computing units), so they can process large amounts of data simultaneously.
- Examples: AI deep learning, machine learning, and simulation calculations.
- Acceleration
- It divides the processing as a supplement to the CPU and increases the overall processing speed.
- It is also used to encode and decode videos.
GPU configuration
- Shader processor
- A basic unit that performs image processing and calculations.
- A large number of parallel operations are possible.
- Memory (VRAM: Video Memory)
- High-speed memory to store image data, textures, and calculation results to be processed.
- The higher the capacity, the more complex the processing possible.
- interface
- It is responsible for connecting with the display (e.g., HDMI or DisplayPort).
Factors that determine GPU performance
- Number of CUDA cores (or stream processors)
- The number of computing units. The more, the higher the performance.
- VRAM capacity
- Affects the processing power of graphics data.
- Clock Frequency
- An indicator of processing speed.
Representative GPU manufacturers
- NVIDIA
- GeForce series (for gaming)
- Quadro, RTX series (for professionals and AI), etc.
- AMD
- Radeon Series (for gaming)
- Instinct series (for data centers and AI), etc.
- Intel
- Arc series (for games) and others
- Built-in GPUs for embedded use are also available.
Main uses of GPUs
- game
- Perform real-time 3D rendering.
- Video Editing CG Production
- Editing high-resolution footage and rendering CG.
- AI Deep Learning
- Learning and inference of large neural networks.
- Scientific and technical computing
- Advanced calculations such as simulation, astronomy, biology, etc.
References
- NVIDIA official website
https://www.nvidia.com/ja-jp/search/?page=1q=GPUsort=relevance - AMD official website
https://www.amd.com - Intel official website (information about GPUs)
https://www.intel.com
What’s New on NVIDIA’s GPUs Becoming Mainstream in AI Development
On NVIDIA’s official website, you can check the list of GPU products for general consumer and corporate use, as well as their performance and pricing information, on the following page.
For general consumer use (GeForce series)
Product List and Performance Comparison: Compare the specifications and features of each model, including the GeForce RTX 50 series.
NVIDIA Official HP: GeForce Graphics Card Comparison
https://www.nvidia.com/ja-jp/geforce/graphics-cards/compare/
Pricing information: Pricing is displayed on each model’s details page.
GeForce RTX 50 Series Graphics Cards
Enterprise (GPU for Data Centers)
https://resources.nvidia.com/l/en-us-gpu#referrer=vanity
Product List and Performance Information: Contains the GPU product lineup for data centers and their performance details.
High-Performance Supercomputing – NVIDIA Data Center GPUs
https://www.nvidia.com/ja-jp/data-center/data-center-gpus/
Pricing information: The prices of corporate products are often not specified on the official website, and you need to contact us directly for detailed pricing.
NVIDIA GPU-Enabled Server Platforms
https://docs.nvidia.com/data-center-gpu/line-card.pdf
NVIDIA Data Center GPU Resource Center
https://resources.nvidia.com/l/en-us-gpu#referrer=vanity
NVIDIA Blackwell Platform Launches to Drive a New Era of Computing
https://www.nvidia.com/ja-jp/about-nvidia/press-releases/2024/nvidia-blackwell-platform-arrives-to-power-a-new-era-of-computing/
NVIDIA Blackwell Architecture
https://www.nvidia.com/en-us/data-center/technologies/blackwell-architecture/?ncid=no-ncid
NVIDIA NV Link: Bidirectional direct GPU-to-GPU interconnect to extend multiple GPU inputs and outputs (IO) in the server
https://www.nvidia.com/en-us/data-center/nvlink
For the latest information and detailed specifications, please check the official website above.
What is the cost of GPU servers for enterprises?
It can be simulated with the GPU estimation simulator provided by NTTPC Communications.
This tool can be useful for estimating the effectiveness of GPU deployment and selecting the right GPU model.
Key features and applications
- Simulating GPU Implementation Effects
- Estimate how much performance improvement and cost reduction can be expected in your business or application.
- Examples: Use cases such as video editing, AI learning, and HPC (high-performance computing).
- Choosing the Right GPU
- Based on the application you want to use and the computing power required, we propose the optimal GPU model.
- Useful if you are considering using NVIDIA’s corporate products or cloud environments.
- Estimating usage costs
- Calculate the cost reduction effect of GPU introduction and the balance between the cost of introduction.
GPU Estimation Simulator (NTTPC Communications)
https://www.nttpc.co.jp/cgi-bin/gpu/simulation/index.cgi
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