How to Scale LLMs with the AI Supercloud

According to O'Reilly's research, 67% of companies use generative AI products powered by LLMs, showing how prevalent these models have become across various industries. But here’s the thing: some companies still struggle to deploy large-scale LLM applications into their operations. Why is that? A major challenge is scaling AI. Businesses are often held back by concerns over the complexity, costs and resources required to scale these technologies. If you're in a similar situation, you're not alone. Read on to discover how the AI Supercloud can help you overcome these scaling hurdles and make LLM adoption a reality for your business. 

The Problem Behind Scaling LLMs 

Scaling LLMs does improve performance but also increases the complexity of managing these systems. As models grow larger, with over 100B parameters, they become more efficient and proficient in tasks like zero-shot and few-shot learning. But scaling to these levels requires advanced infrastructure such as high-performance GPUs, optimised storage systems and specialised expertise which can be costly for many companies. There are also human-in-the-loop evaluations, necessary to ensure the quality and relevance of LLM outputs which become difficult to scale. If the evaluation involves testing and refining large language models (LLMs) in real-time, high-performance hardware and fast networking may be required to ensure the model generates responses quickly enough for effective human feedback. The complexity and time involved in such LLM evaluations further escalate the challenge.  

Choosing the AI Supercloud for Scaling LLMs 

Such challenges make it clear why many companies hesitate to deploy large-scale LLMs. The AI Supercloud offers a scalable solution designed specifically for AI workloads to tackle these complexities: 

Access to Cutting-Edge Hardware 

On the AI Supercloud, you have access to the latest NVIDIA GPUs like the NVIDIA HGX H100 and NVIDIA HGX H200 designed for large-scale ML workloads. These GPUs offer the massive computational power required to train and deploy LLMs effectively. 

Scalable AI Clusters 

Does your business need flexibility? The AI Supercloud allows you to dynamically scale your AI clusters based on the size of your training dataset, giving you the resources to handle any LLM workload, from small to massive. 

Liquid Cooling for Optimal Performance 

Training large language models can lead to intense heat generation which can impact hardware performance. The AI Supercloud offers liquid cooling for optimal performance and longevity of the hardware while maintaining consistent high speeds throughout intensive AI tasks like LLM training.  

Burst Scalability with Hyperstack 

If your LLM requires extra compute resources during peak times, you can burst into additional capacity with Hyperstack for flexibility and cost efficiency without long-term commitments. Hyperstack is our GPUaas platform that offers instant access to high-end GPUs like the NVIDIA H100 and NVIDIA A100 through a pay-per-use pricing model. 

High-Performance Storage and Networking 

LLMs require fast data processing and storage capabilities. The AI Supercloud integrates NVIDIA-certified WEKA storage solutions with GPUDirect Storage for fast data transfer between GPUs and storage  

With NVIDIA Quantum-2 InfiniBand networking, you also get low-latency connections required for AI workloads for smooth communication between compute nodes in a distributed LLM training environment. 

Expert Technical Support 

Scaling is one thing, but managing LLMs is even more complex. With our dedicated Technical Account Managers and MLOps engineers, we ensure you receive continuous support through every step of the LLM deployment.  

How to Get Started with Scaling LLMs on the AI Supercloud 

Ready to scale your LLMs? Don't wait any longer- your LLM journey begins now and we're here to make your success our mission. Here’s how to get started with the AI Supercloud: 

Step 1: Assess Your AI Needs 

Before scaling your LLMs, assess your AI and infrastructure requirements. This involves understanding the computational demands of your specific LLM models, such as the size of the dataset, complexity of the training process, hardware requirements and the expected time to deploy. A discovery call with our solutions engineers can help you evaluate these needs and determine the best configurations for your workloads.  

Step 2: Customise Your Configuration 

Once your needs are assessed, our team will propose personalised hardware and software configuration. This ensures that your infrastructure is perfectly aligned with the demands of your LLM workloads.  

Step 3: Get End-to-End Services 

With AI Supercloud, you get end-to-end services including fully managed infrastructure, software updates, and security. We offer tailored MLOps-as-a-Service, integrate custom software solutions, and provide optimised, fully managed Kubernetes or SLURM environments to meet your specific needs. 

Step 4: Run a Proof of Concept (PoC) 

It’s a good idea to run a Proof of Concept (PoC) on the customised environment to assess its performance and compatibility with your existing systems. 

Step 5: Scale and Deploy Your LLM 

Once your PoC is successful, we'll guide you through onboarding, migration and integration. With Hyperstack’s burst scalability, you can also scale your infrastructure dynamically based on the needs of your LLM projects.  

Conclusion 

Scaling LLMs is not an easy one but with the AI Supercloud, you can ensure that your infrastructure is up to the mark. With cutting-edge hardware, personalised solutions and expert support, the AI Supercloud helps businesses scale their LLMs effectively while managing costs and boosting 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 

FAQs 

What hardware does the AI Supercloud offer for LLM scaling? 

The AI Supercloud provides the latest NVIDIA GPUs, such as the NVIDIA HGX H100 and NVIDIA HGX H200, optimised for large-scale AI and ML workloads, ensuring powerful and efficient LLM training and deployment. 

How does the AI Supercloud help with scaling LLMs dynamically?

The AI Supercloud allows you to scale AI clusters dynamically, offering flexibility to meet the computational demands of both small and massive LLM models without the need for over-provisioning resources. 

What makes the AI Supercloud's storage suitable for LLMs? 

With NVIDIA-certified WEKA storage and GPUDirect Storage, the AI Supercloud ensures fast data transfer between GPUs and storage, eliminating bottlenecks for LLM training while maintaining high-speed data processing. 

How does the AI Supercloud manage peak compute demands? 

The AI Supercloud offers burst scalability through Hyperstack, allowing you to scale up resources on-demand during peak training periods, providing flexibility and cost efficiency without long-term commitments. 

What support does the AI Supercloud offer for scaling LLMs? 

The AI Supercloud provides expert guidance through dedicated Technical Account Managers and MLOps engineers, ensuring your LLMs are optimised and deployed effectively with continuous support throughout the journey.