NVIDIA AI Hardware: A Review
March 31, 2023
What do cloud gaming and ChatGPT have in common?

NVIDIA AI Hardware

Before Christmas I wrote an article on gaming Chromebooks and I now realize that the servers powering online games share many similarities with those powering ChatGPT.

We all know that artificial intelligence is the future, and it is rapidly changing the way we live and work. The ChatGPT AI model is an excellent example of how AI can be applied to make our lives easier, by providing intelligent responses to our questions and requests. However, to make ChatGPT come alive, it needs a powerful GPU, and that is where NVIDIA comes in.

In this article, we will discuss the power of the NVIDIA GPU for ChatGPT, and why it is essential for the AI model to function optimally. We will also explore the various features of the NVIDIA GPU and how it enhances the performance of ChatGPT.

The Power of NVIDIA GPU

The NVIDIA GPU is a high-end graphics processing unit that is specifically designed to handle complex graphics and data processing tasks. It has been widely adopted by the gaming industry (GeForce Now) for its exceptional performance in rendering 3D graphics in real-time. Google introduced three gaming Chromebooks in October of 2022 which leveraged cloud gaming running on this platform for a great user experience. However, the NVIDIA GPU is not limited to the gaming industry and is also widely used in various applications, including artificial intelligence. AI is all about large data sets which can often exceed a million data points. Imagine working on a spreadsheet which contained a million cells of data.

The NVIDIA GPU is a crucial component in the ChatGPT AI model, as it provides the necessary computational power to process complex data and generate intelligent responses. The NVIDIA GPU is specifically designed for parallel processing, which makes it ideal for AI applications that require extensive processing power.


The A100 80GB GPU has 6912 CUDA Cores and 80 GB VRAM running at 3.2Gbit/s with a memory bandwidth of 2TB/sec. In addition, each GPU has 432 Tensor Cores. The interesting thing is the SXM4 version is not sold as a unit of one, but instead in bundles of 2, 4, or 8. Each processor is linked to the others and becomes an amalgamation of the total number of processors acting as one. 

Features of NVIDIA GPU

The NVIDIA GPU is known for its exceptional performance, and it comes with various features that make it ideal for AI applications. Some of the essential features of the NVIDIA GPU include:

  • CUDA Cores - The CUDA cores are the processing units that handle the parallel processing tasks. The more CUDA cores a GPU has, the more parallel processing tasks it can handle.
  • Memory Bandwidth - Memory bandwidth is the rate at which data can be transferred from the GPU's memory to the processor. The higher the memory bandwidth, the faster the GPU can access data.
  • VRAM - The VRAM is the memory dedicated to the GPU, and it is essential for handling large datasets.
  • Tensor Cores - The Tensor Cores are specifically designed for deep learning applications and are capable of processing large amounts of data in parallel.

How NVIDIA GPU Enhances ChatGPT Performance

The NVIDIA GPU enhances the performance of ChatGPT in several ways, including:

  • Parallel Processing - The NVIDIA GPU's parallel processing capability allows ChatGPT to process data faster and generate intelligent responses in real-time.
  • Deep Learning - The NVIDIA GPU's Tensor Cores are specifically designed for deep learning applications, making ChatGPT more accurate and reliable.
  • Large Dataset Handling - The NVIDIA GPU's VRAM is essential for handling large datasets, making it possible for ChatGPT to handle complex data and generate intelligent responses.


The cost for the data center SXM4 version is around $15,000. A fully loaded HGX with all the trimmings exceeding $200,000.


It is important to acknowledge that NVIDIA is not the only company operating in this particular field. Google also has a product called Tensor Processing Units, which are a key component of the Google Cloud and possess their own processing capabilities. The v4 TPU chip, for example, is made up of two TensorCores, with each TensorCore having a vector unit, a scalar unit, and four MXUs. The Matrix-multiply unit, or MXU, is a systolic array consisting of 128x128 multiply/accumulators that supply most of the computational power in the TPU core. A single chip can handle up to 276 teraflops of processing and is connected to 32GB of VRAM rated at 1200 GB of bandwidth. Like NVIDIA, the TPUs can be clustered together for increased performance.

Data sheet (PDF)