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:
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:
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:
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.
Enterprises are integrating LLM-powered assistants into their internal workflows to improve productivity and streamline operations, for example:
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.
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:
The hopper generation NVIDIA H100 GPUs offer:
The NVIDIA H200 is built on the same architecture with improved memory capacity and efficiency for large-scale LLM training.
The NVIDIA Blackwell GB200 is the next generation of AI hardware, designed to accelerate LLM training and inference performance.
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:
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-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.
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.
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.
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.
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.
Enterprises need powerful GPUs like NVIDIA HGX H100/NVIDIA HGX H200, high-speed storage, and low-latency networking for efficient LLM training at scale.
Training a large LLM can take weeks or months, depending on the model size, dataset, and GPU infrastructure used.
The AI Supercloud provides scalable, high-performance GPU clusters with NVIDIA HGX H100/H200 and ultra-low-latency networking for seamless AI workloads.
The AI Supercloud integrates high-performance NVMe storage with NVIDIA-certified WEKA storage and GPUDirect Storage to minimise latency and maximise data throughput.
Yes, enterprises can start with a small cluster and seamlessly scale up with Hyperstack’s on-demand GPU bursting to handle peak training demands.