Introduction

In 2025, custom AI solutions and Large Language Models (LLMs) became game changers for global businesses. LLMs leverage Deep Learning (DL) and Machine Learning (ML) capabilities to understand and generate human language text by analyzing large datasets.  

LLMs can generate source code for programmers. Other possible use cases may include online search, customer service, DNA research, or sentiment analysis. Today, global businesses and enterprises need effective GPU clusters, networking, and storage systems.

AI platforms and LLMs play a pivotal role in Software as a Service (SaaS), healthcare, and the gaming industry. This article will explore the rise of LLMs, LLMs’ demand for HPC, the essentials of AI platforms, and their real-world use cases.

The Rise of LLMs

An LLM is a deep learning model used for recognizing, summarizing, translating, predicting, and generating text and content by analyzing large datasets.

In 2010, the rise of neural networks with word embeddings like GloVe and Word2Vec that enable models to learn semantic relationships was a major shift. Sequential data was handled through sequence models, such as Long Short-term Memory (LSTM) and Recurrent Neural Networks (RNNs).

Later on, Vaswani et al. (2017) conducted extensive research on encoder-decoder transformer architecture to train models on massive datasets.

In 2018, Google’s BERT, an encoder-only transformer, and OpenAI’s GPT (Generative Pretrained Transformer) were other landmarks for LLMs. GPT 2 version was introduced in 2019, while GPT 3 began its journey in 2020 with 175 billion parameters, establishing LLM as a powerful force in AI technology. After that, OpenAI introduced ChatGPT and GPT-4 in 2022 and 2023, respectively. Currently, many AI models have been developed to revolutionize technologies. Some of these models include Google’s Gemini, Grok 4, SuperBot, and Anthropic’s Claude.

Why Do You Need LLMs for HPC?

LLMs play a significant role in High-Performance Computing (HPC) infrastructure. With its ML and DL capabilities, LLM solutions can enable automation, data analysis, and enhanced computational workflows.

In addition, LLMs optimize HPC environments by processing large datasets and simply various tasks, such as automatically generating codes for parallel computing.

4-8 petaFLOPS is a special feature that LLM leverages for FP8 precision. NVIDIA H100 or H200 is an example of these models.

What Businesses Should Look for in AI Platforms?

Today, dozens of AI platforms dominate the IT and GPU industry. However, businesses seek AI platforms that feature powerful GPU clusters, reliable and fast networking, and high-speed storage solutions to support their AI workloads. The following sections delve into detail.

GPU Clusters

GPU clusters, also known as multi-node GPUs, are interconnected computing nodes that enhance performance and facilitate parallel computing.

In fact, GPU clusters help build production-level AI systems by integrating with RAG pipelines, inference frameworks, file storage, and vector databases. GPU clusters can also be used for LLM fine-tuning and training to scale operations, speed up task completion time, and optimize overall operation.

Moreover, GPU clusters or multi-node systems are ideal for data-intensive applications, AI model training, and cloud computing services. An example of a GPU cluster-based AI platform is NVIDIA DGX Cloud on CSPs.

Networking

AI platforms must feature robust networking to deal with a large data flow among compute nodes, storage systems, and external data sources.

Moreover, networking must offer route convergence with proactive link monitoring. Failover enhancements are also essential to ensure minimal downtime with faster failovers.

Storage

Since LLMs process large datasets, requiring a low-latency, scalable storage for embeddings, training data, and RAG retrieval is essential. To this end, object storage and vector databases play a crucial role.

Unified objects, such as Google Cloud Storage and AWS S3, with SSD overlays can address unstructured data lakes. On the other hand, vector databases store pieces of information as vectors or mathematical representations.

More importantly, the storage system should be integrated with GPU clusters or GPU Direct Storage to enable data transmission directly to GPU memory.

Real-World Use Cases

Healthcare

Researchers in healthcare leverage GPU-accelerated simulations to improve diagnostic tools and develop life-saving drugs. NVIDIA AI enterprise products can help gather insights from real-world data to enhance clinical trial processes and overall system progress.

Gaming

AI platforms can provide gamers with an unprecedented experience. Real-time AI interactions and optimized graphics make a significant shift in the gaming industry. NVIDIA RTX-powered AI offers optimized video conferencing, fastest editing, and the sharpest streaming. This technology provides the fastest performance in AI-accelerated games.

SaaS Applications

Custom AI platforms can help enhance user experience, support personalization at scale, and automate content creation and complex workflows.

Bitworks – World-leading Infrastructure Provider

As demands for AI workloads and LLMs are accelerating, Bitworks has emerged as the most trustworthy and reliable infrastructure provider. The company delivers scalable and efficient GPU clusters, swift networking, and low-latency storage solutions appropriate for an HPC environment.

More importantly, Bitworks can empower businesses to enhance resource allocation, accelerate model training, and support seamless growth in AI-powered applications. Contact us to book your order.