Scaling AI for High-Performance Workloads
It is common for enterprises to struggle while scaling AI solutions due to high-performance demands and complex infrastructure. Here are the primary challenges that enterprises face when scaling AI for high-performance workloads:
- Insufficient Computational Power: AI models and Deep learning workloads require massive computing power for tasks like training neural networks. Traditional CPUs cannot handle the volume of parallel computations necessary to effectively process large datasets and complex algorithms.
- Data Bottlenecks: AI workloads are highly data-intensive, requiring vast amounts of information to train models. If your infrastructure isn't designed for fast data access, the system will experience data bottlenecks, severely impacting performance.
- Limited Scalability: As AI models become more complex, enterprises must scale their infrastructure to accommodate increasing workloads. However, many businesses find it difficult to scale their hardware and software solutions without completely overhauling their infrastructure.
- Slow Data Exchange: AI workloads are often spread across multiple nodes, so fast communication between them is imperative. Slow inter-node communication can severely hinder AI model performance.
What are GPU Clusters for AI?
GPU Clusters for AI are networks of interconnected GPUs that work together to perform large-scale workloads. These clusters are optimised to run complex algorithms, large datasets and deep learning models. Each GPU within the cluster processes a portion of the overall workload for parallel computing in AI. This drastically reduces the time required to train complex models and perform inference, making GPU clusters far more efficient than traditional CPU-based systems for high-performance AI workloads.
How Enterprises Can Scale AI with GPU Clusters with the AI Supercloud
Building scalable GPU Clusters for AI involves integrating high-performance GPUs, efficient storage, fast networking and effective management solutions. With the AI Supercloud, enterprises can easily start scaling AI Infrastructure without sacrificing performance.
1. High-Performance GPUs for AI
Powerful and advanced GPUs can process many tasks simultaneously, reducing the time it takes to train models and run inference tasks. On the AI Supercloud, we offer the latest NVIDIA hardware, built with reference architecture in partnership with NVIDIA, such as:
- NVIDIA HGX H100: Designed for high-performance computing (HPC) and AI workloads, the NVIDIA H100 offers unparalleled speed and efficiency, capable of processing complex AI models and running large-scale data analysis in record time.
- NVIDIA HGX H200: Designed to handle intensive AI workloads, the NVIDIA HGX H200 offers exceptional scalability and processing power for enterprises looking to deploy AI at scale.
- NVIDIA Blackwell GB200 NVL72/36: Built on the revolutionary Blackwell architecture, the NVIDIA GB200 NVL72/36 GPUs are specifically designed for Generative AI training and inference at scale with superior throughput and low-latency processing.
You May Also Like to Read: NVIDIA Blackwell vs NVIDIA Hopper: A Detailed Comparison
2. Faster Networking
The networking between the nodes in GPU Clusters for AI must be optimised for speed and low latency. Our GPU Clusters for AI are equipped with NVIDIA Quantum-2 InfiniBand with speeds up to 400Gb/s, this interconnect technology facilitates high-speed data exchange. Fast inter-node communication is crucial for reducing latency and preventing performance bottlenecks, particularly in real-time AI applications where speed is essential. These advanced networking solutions enable enterprises to build GPU Clusters for AIs that scale effortlessly while maintaining low latency and high throughput.
3. High-Performance Storage Systems
To build scalable GPU Clusters for AI, enterprises must have access to high-performance storage systems. AI workloads generate vast amounts of data that must be stored, processed, and accessed quickly. To ensure that AI models have immediate access to these datasets, the storage system needs to be optimised for speed and reliability.
On the AI Supercloud, we use NVIDIA-certified WEKA storage which integrates GPUDirect technology to eliminate data transfer bottlenecks. GPUDirect allows data to be transferred directly between GPUs and storage, bypassing the CPU and reducing unnecessary data movement. This results in ultra-fast data throughput and ensures that large datasets are readily available for AI training and inference tasks.
4. Fully Managed Kubernetes
One of the biggest challenges enterprises face when building scalable GPU Clusters for AI is managing the complexity of the infrastructure. From provisioning GPUs to managing distributed computing resources, it can be difficult to ensure everything runs smoothly and efficiently. The AI Supercloud addresses this by integrating Fully Managed Kubernetes, a powerful open-source container orchestration platform into our GPU clusters for high-performance AI workloads. Fully Managed Kubernetes automates the deployment, scaling and management of AI workloads, ensuring that resources are used efficiently and that the system can scale based on demand. With our Fully Managed Kubernetes, enterprises can focus on innovation and development, while the AI Supercloud handles the backend infrastructure management.
5. Customisable Configurations for Scalability
Our GPU Clusters for AI could be customised to suit unique high-performance workloads for enterprises. Our solutions include options for:
- GPU, CPU and RAM configurations: Enterprises can select the optimal hardware specifications for their AI workloads, ensuring maximum performance and scalability.
- Liquid cooling: For enterprises dealing with high-density clusters, liquid cooling solutions can be implemented to ensure optimal performance while preventing overheating.
- Middleware options: Enterprises can choose the most suitable middleware and management solutions such as Kubernetes to streamline operations and reduce complexity.
Scale AI with the AI Supercloud
If you're ready to start scaling your operations, book a call with our specialists to discover the best solution for your project’s budget, timeline, and technologies.
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FAQs
What are GPU clusters for AI?
GPU clusters for AI are networks of interconnected GPUs that work together to process large datasets and run complex AI models, significantly reducing computation time compared to traditional systems.
How do GPU clusters benefit AI workloads?
GPU clusters enable parallel processing, handling multiple tasks simultaneously, allowing AI models to scale effectively and run faster for large-scale machine learning and deep learning applications.
How can enterprises scale their AI infrastructure?
Enterprises can scale AI infrastructure by using GPU clusters with powerful hardware like NVIDIA GPUs, high-speed networking and efficient storage systems, which the AI Supercloud provides for optimal performance.
What GPUs are used in the AI Supercloud for AI workloads?
The AI Supercloud uses cutting-edge GPUs like the NVIDIA HGX H100, H200, and NVIDIA Blackwell GB200, designed specifically for high-performance computing and AI workloads such as AI Model Training with GPUs.
How does the AI Supercloud optimise data storage for AI workloads?
The AI Supercloud uses NVIDIA-certified WEKA storage with GPUDirect technology, enabling direct data transfer between GPUs and storage, ensuring high-speed access to datasets without CPU bottlenecks.
October 1, 2024
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