A Guide to LLM Training in Enterprises in 2025

How Enterprises Are Using LLMs in 2025

Over the last two years, enterprises have been integrating LLMs into their products and workflows. By mid-2024, 65% of enterprises reported actively using LLMs, nearly double the percentage from late 2022​ (according to McKinsey). But how are enterprises using LLMs exactly? Check out below:

Boosting Business Operations

What started as experimental pilots have now become large-scale enterprise deployments, spanning customer support, marketing automation, software development and knowledge management. Companies are using LLM-driven AI across various functions including:

• AI Coding Assistants:  LLMs integrated into developer tools (IDEs) are boosting productivity by 40% helping engineers write, debug and optimise code faster.
• Customer Support: LLMs are being trained to automate responses, reducing wait times and enhancing customer satisfaction.
• Marketing Automation: LLMs can generate personalised content, ad copies and email campaigns at scale.

Fine-Tuning vs. Training LLMs from Scratch

Enterprises are carefully evaluating whether to build their LLMs from scratch or use third-party models. Many are choosing a hybrid approach, starting with a pre-trained foundation model and fine-tuning it with proprietary data. This strategy accelerates deployment, reduces computational costs and can improve domain-specific performance such as finance, legal and scientific applications without the need for extensive training from the ground up.

For example, enterprises are using open-source LLMs such as Meta’s Llama 2 and Llama 3, EleutherAI’s GPT-J/GPT-NeoX and Bloom and fine-tuning them at scale for industry-specific applications in finance, law and scientific research. According to NVIDIA’s LLM benchmarks, the advent of highly capable community LLMs has given enterprises a wealth of pre-trained models that can be efficiently fine-tuned for specialised use cases, minimising the need for full-scale training while maintaining high accuracy and relevance.

On the other hand, some large enterprises with unique data or needs are training custom models from the ground up, for example:

• BloombergGPT for finance
• Jurassic-2 for legal applications
• Med-PaLM for healthcare

These are some prime examples of industry-specific LLMs designed to excel in different domains. Enterprises have started to recognise that smaller and targeted models (10B-50B parameters) can often outperform larger, general-purpose models (100B+ parameters) when trained on high-quality, domain-specific data. Not only do these models deliver better accuracy for specialised tasks but they are also more cost-effective to train and fine-tune. Hence, many enterprises are curating domain datasets (e.g. all their proprietary documents/emails or industry literature) and either fine-tuning or pre-training medium-sized LLMs on that.

Internal Workflows 

Enterprises are integrating LLM-powered assistants into their internal workflows to improve productivity and streamline operations, for example:

• HR and Policy Automation: Assisting HR teams in drafting policy documents, employee communications, and compliance reports.
• Knowledge Management: Fine-tuning LLMs on internal knowledge bases, wikis, and PDFs, enabling employees to retrieve company-specific information through natural language queries.
• Support Automation: Training LLMs on support tickets and product documentation to automate responses or improve human agent efficiency.

To ensure continuous improvement, these AI-driven systems are monitored and optimised through MLOps pipelines, so enterprises can refine their models based on real-world feedback and evolving business needs.

How Powerful GPUs Are Accelerating Enterprise LLM Training

Over the last 1-2 years, NVIDIA has rolled out new GPU generations like the NVIDIA Hopper and NVIDIA Blackwell that boost LLM training performance for enterprises. These GPUs are designed to handle trillion-parameter models, reduce training times and optimise LLM performance. Here’s how these GPUs compare for enterprise LLM training:

NVIDIA H100

The hopper generation NVIDIA H100 GPUs offer:

• Up to 9x faster training performance compared to the NVIDIA A100, thanks to fourth-gen Tensor Cores and FP8 precision, which doubles FLOPs per cycle for matrix operations in large-scale LLM training.
• 80GB of HBM3 memory with 3.35 TB/s bandwidth, a 67% improvement over previous GPUs, ensuring faster data throughput for LLM training workloads.
• 900 GB/s NVLink bandwidth, allowing efficient multi-GPU communication for large-scale model training.

NVIDIA H200

The NVIDIA H200 is built on the same architecture with improved memory capacity and efficiency for large-scale LLM training.

• 141GB of HBM3e memory per GPU, a 76% increase over the 80GB in NVIDIA H100 for larger batch sizes and reduced model sharding.
• 4.8 TB/s memory bandwidth, 43% higher than NVIDIA H100, ensuring LLM workloads can process and load data faster.
• With more memory per GPU, enterprises can train large models with fewer GPUs, reducing infrastructure complexity and cost.
• NVIDIA H200 GPU provides up to a 47% performance boost over the NVIDIA H100 in MLPerf Training benchmarks.

NVIDIA Blackwell GB200

The NVIDIA Blackwell GB200 is the next generation of AI hardware, designed to accelerate LLM training and inference performance. 

• Up to 2x the training performance of NVIDIA H100, based on MLPerf Training 4.1 benchmarks.
• NVIDIA GB200’s 8TB/s bandwidth accelerates LLM training with faster data transfer speeds over NVIDIA H200’s 4.8TB/s.
• Up to 576 NVIDIA GB200 GPUs can be interconnected with ultra-high bandwidth for efficient multi-node scaling.

Infrastructure Requirements for LLM Training in Enterprises

LLMs have become central to modern enterprise AI strategies, especially following the breakthrough of generative AI in the last couple of years. To support large-scale training, enterprises must carefully design their infrastructure to balance performance, scalability and efficiency. Check out the infrastructure requirements below:

Compute Power

LLM training relies on large-scale GPU clusters to handle complex computations efficiently. Powerful GPUs like the NVIDIA HGX H100 or NVIDIA HGX H200 deliver thousands of teraflops of performance to enable parallel processing required for training models with tens or hundreds of billions of parameters. Training a single large model can take weeks or even months, so it is essential to scale workloads across hundreds or thousands of GPUs to reduce training time. For instance, OpenAI’s GPT-3 was trained on a cluster of 1024 GPUs, and many current projects use similar scales. 

The AI Supercloud offers the latest and optimised NVIDIA HGX H100, NVIDIA HGX H200 and upcoming NVIDIA Blackwell GB200 NVL72/36 to deliver best-in-class compute for LLM training. Our GPU AI clusters are designed for high-performance workloads, ensuring optimal efficiency and performance. If additional capacity is required, enterprises can burst seamlessly into Hyperstack for flexible, on-demand GPU scaling.

High-Performance Storage

High-throughput storage is critical for fully using GPUs during training. LLM datasets are vast, often spanning terabytes to petabytes and require storage systems capable of delivering data with minimal latency. To prevent I/O bottlenecks that could slow down training, enterprises commonly use high-performance storage solutions to optimise data retrieval speeds.

The AI Supercloud integrates NVIDIA-certified WEKA storage with GPUDirect Storage support, ensuring ultra-fast data transfer rates and minimal latency. Our high-performance NVMe and distributed file storage solutions are optimised for AI workloads, eliminating bottlenecks and maximising GPU efficiency.

Ultra-Low-Latency Networking

In distributed LLM training, high-speed networking is imperative for maintaining efficient communication between compute nodes. Frequent data exchanges, including gradient synchronisation and parameter updates, necessitate ultra-low-latency and high-bandwidth connections. Enterprises can use advanced networking technologies such as NVIDIA NVLink/NVSwitch to facilitate high-speed intra-server GPU communication, while InfiniBand enables rapid data transfers between compute clusters. 

The AI Supercloud provides NVIDIA Quantum-2 InfiniBand in our GPU clusters to provide high-bandwidth, ultra-low-latency interconnects for seamless distributed training. These cutting-edge networking solutions accelerate gradient updates and ensure synchronisation across multi-GPU clusters, reducing training time and improving model efficiency.

Scalability 

Investing in scalable solutions is imperative for enterprises training LLMs as model complexity, dataset sizes and computational demands continue to grow. A scalable infrastructure ensures that enterprises can expand their AI capabilities without frequent hardware overhauls or excessive costs. Without scalability, enterprises risk running into resource limitations, inefficient training pipelines and longer development cycles, which can hinder innovation.

The AI Supercloud provides customisable, scalable AI clusters built on NVIDIA’s best practices. Enterprises can start with a smaller cluster and scale up seamlessly as workloads grow. Our on-demand GPU bursting with Hyperstack allows businesses to handle peak training demands without overprovisioning, keeping costs efficient while maintaining high performance.

Conclusion

Enterprises need robust, scalable and high-performance infrastructure to train and fine-tune LLMs effectively. The AI Supercloud delivers cutting-edge scalable solutions like optimised  GPU clusters with high-speed storage, ultra-low-latency networking and liquid cooling to accelerate LLM training while optimising efficiency. Whether you need large-scale AI clusters or on-demand GPU access, the AI Supercloud ensures seamless scalability and top-tier performance. 

If you want to get started, book a call with our specialists to discover the best solution for your project’s budget, timeline and technologies. 

Book a Discovery Call 

Explore Related Resources

• Enterprise Challenges in AI Adoption and How to Overcome Them

• How to Scale LLMs with the AI Supercloud

• A Guide to Generative AI in the Enterprise

• 10 Most Popular Use Cases of AI in Enterprise in 2025

FAQs

What is the difference between fine-tuning and training an LLM from scratch?

Fine-tuning involves adapting a pre-trained model with domain-specific data, making it faster and more cost-effective than training a model from scratch.

What are the key hardware requirements for LLM training?

Enterprises need powerful GPUs like NVIDIA HGX H100/NVIDIA HGX H200, high-speed storage, and low-latency networking for efficient LLM training at scale.

How long does it take to train an enterprise-grade LLM?

Training a large LLM can take weeks or months, depending on the model size, dataset, and GPU infrastructure used.

What makes the AI Supercloud ideal for enterprise LLM training?

The AI Supercloud provides scalable, high-performance GPU clusters with NVIDIA HGX H100/H200 and ultra-low-latency networking for seamless AI workloads.

How does the AI Supercloud handle storage and data transfer?

The AI Supercloud integrates high-performance NVMe storage with NVIDIA-certified WEKA storage and GPUDirect Storage to minimise latency and maximise data throughput.

Can the AI Supercloud scale on demand?

Yes, enterprises can start with a small cluster and seamlessly scale up with Hyperstack’s on-demand GPU bursting to handle peak training demands.