NVIDIA has evolved from a graphics card manufacturer into a dominant force in artificial intelligence (AI) and high-performance computing (HPC). This transformation's core is NVIDIA data centers, which power AI model training. This is because NVIDIA has built a data center ecosystem that sets it apart from traditional data centers.

What is NVIDIA?

NVIDIA is an American multinational technology company specializing in graphics processing units (GPUs), AI hardware and software, and high-performance computing solutions. Founded in 1993 by Jensen Huang, Chris Malachowsky, and Curtis Priem, the company initially focused on graphics processing for gaming but has since expanded into AI, data centers, and cloud computing.

How NVIDIA Became a Tech Giant

NVIDIA’s rise to dominance can be attributed to several key factors. The company pioneered the GPU gaming and professional visualization market through the GeForce and RTX series. But its real breakthrough came with CUDA, a platform that transformed GPUs into powerful AI and high-performance computing tools.

A strategic shift occurred when AI adoption skyrocketed, and NVIDIA capitalized on this opportunity by designing GPUs specifically for deep learning and machine learning workloads. The acquisition of Mellanox Technologies in 2019 gave NVIDIA a crucial foothold in high-performance networking for AI-driven data centers.

NVIDIA's biggest leap came during the AI boom, as companies like OpenAI, Microsoft, Google, and Meta integrated NVIDIA’s data center GPUs to train massive AI models. Today, NVIDIA’s GPUs power everything from ChatGPT to Tesla’s self-driving AI.

With a market valuation exceeding $3.3 trillion in 2024, NVIDIA is at the heart of the AI revolution, providing the computing backbone for the world’s most advanced technologies.

How Are NVIDIA Data Centers Different?

Traditional data centers are designed for general-purpose computing, cloud storage, and web hosting, relying primarily on CPUs from companies like Intel and AMD.

In contrast, NVIDIA data centers are specifically built to handle AI, machine learning, and HPC workloads.

One of the biggest distinctions is that NVIDIA data centers rely on GPUs instead of CPUs. While CPUs are excellent for sequential processing, they struggle with the massive parallelism required for AI workloads. In contrast, NVIDIA’s GPUs, equipped with CUDA and Tensor cores, can process thousands of AI operations simultaneously.

Networking is another key difference. Traditional data centers use standard Ethernet connections, which create bottlenecks when moving large amounts of data. NVIDIA, however, integrates Mellanox InfiniBand and NVLink, providing high-speed, low-latency communication between GPUs.

Scalability is also a defining feature. Unlike general-purpose cloud infrastructure, NVIDIA data centers support multi-GPU supercomputing clusters like DGX SuperPOD, which can scale AI workloads across multiple GPUs. This makes NVIDIA’s infrastructure ideal for training large-scale AI models and deep learning research.

NVIDIA also builds AI-first software solutions that are deeply integrated into its hardware. Instead of relying on generic enterprise software, NVIDIA data centers run AI-optimized frameworks like CUDA, Triton Inference Server, and NVIDIA AI Enterprise.

Energy efficiency is another critical distinction. AI models consume vast amounts of power, but NVIDIA has invested in AI-driven workload optimization to reduce energy waste. Liquid cooling solutions and intelligent power management make NVIDIA data centers more sustainable than traditional enterprise data centers.

Finally, NVIDIA data centers play a direct role in powering AI breakthroughs. While most data centers focus on hosting websites or storing enterprise data, NVIDIA’s infrastructure is actively used to train AI models for OpenAI, Google, Microsoft, Tesla, and many other leading AI-driven companies.

How NVIDIA Data Centers Power AI

One of the most significant AI-driven companies, OpenAI, relies on NVIDIA’s GPUs to train and deploy its large-scale AI models, including ChatGPT and DALL-E.

OpenAI uses NVIDIA A100 and H100 Tensor Core GPUs to train its AI models, leveraging their high-speed parallel processing capabilities. These GPUs enable OpenAI to process vast datasets quickly, allowing AI models to improve through continuous learning.

To support its AI workloads, OpenAI operates on cloud platforms like Microsoft Azure, which integrates NVIDIA GPUs for large-scale AI model training. This allows OpenAI to scale its computing power dynamically as demand increases.

For real-time AI inference, NVIDIA’s Triton Inference Server ensures that models like ChatGPT can process millions of queries efficiently. Without NVIDIA’s GPUs and data center technology, OpenAI’s advancements in generative AI wouldn’t be possible at their current scale.

Key Components of NVIDIA Data Centers

High-Performance Data Center GPUs

• H100 Tensor Core GPU (Hopper Architecture) – Optimized for AI model training, large-scale inference, and generative AI.

• A100 Tensor Core GPU (Ampere Architecture) – Designed for AI, data analytics, and cloud computing.

• L40 and A40 GPUs – Used in AI-enhanced graphics, video processing, and professional visualization.

AI Software Stack

• CUDA – A parallel computing platform that allows AI developers to use NVIDIA GPUs efficiently.

• NVIDIA AI Enterprise – A full-stack suite for building and deploying AI applications.

• Triton Inference Server – Optimizes AI model deployment across NVIDIA GPUs.

Networking and Data Processing

• Mellanox InfiniBand – Low-latency, high-speed networking for AI clusters.

• NVLink – Enables multiple GPUs to communicate efficiently.

• BlueField DPUs – Offloads networking and security workloads to improve efficiency.

Power and Cable Infrastructure in AI Data Centers

• EPR/PVC power cables – For high-efficiency power distribution.

• MV-105 cables – Used in medium-voltage power applications for AI servers.

• THHN/THWN cables – Standard building wire for electrical wiring in data centers.

• Type W cables – Heavy-duty power cables designed for high-performance AI computing environments.

Conclusion

NVIDIA data centers are fundamentally different from traditional data centers—they are purpose-built for AI, machine learning, and high-performance computing, rather than general enterprise workloads.

If you are looking for cables to power your own AI data center, Nassau National Cable has a catered offering of cables, such as  EPR/PVC power cables, MV-105, THHN/THWN, and Type W.