Scaling Cloud Computing to Meet the Needs of AI Workloads

Artificial Intelligence (AI) is perhaps the most disruptive element in computing. From behemoth language models like GPT and Gemini to autonomous vehicles and predictive analytics, AI workloads are pushing the boundaries of what cloud computing can offer. To enable that growth, cloud providers must become bigger as well as smarter, more specialized, and more responsive.

The Nature of AI Workloads

Relative to traditional web or transactional workloads, AI workloads present several unique challenges:

• Extremely large-scale parallel processing demands (e.g., running a transformer model on thousands of GPUs).
• Use of high-throughput data from diverse sources.
• Use applications like fraud detection or autonomous navigation with latency-critical inference pipelines.
• Dynamic experimentation, in which workloads shift rapidly in response to model iterations.

These needs cannot be fulfilled by commodity infrastructure,they demand tailor-made solutions at every level of the cloud stack.

Challenges for Cloud Providers

Compute Constraints

Demand driven by both startups and Big Tech exceeds supply of TPUs, GPUs, and other accelerators. Tens of thousands of GPUs working for months were reportedly needed to train a frontier model like GPT-4.

Storage and Data Access

AI workloads consume and generate petabytes of data. Storage must be scalable with high IOPS, low latency, and versioning, streaming, and vector retrieval capability.

Network Architecture

Distributed training demands ultra-high-speed interconnects and low-latency synchronization—much more than present virtualized networks can provide.

Energy Efficiency and Sustainability

A single training of a large model can consume as much power as hundreds of American homes in a year. Placement of workload and cooling effectiveness are of utmost business importance these days.

How Cloud Providers Are Responding

1. Dedicated AI Infrastructure

AWS launched AWS Trainium and Inferentia chips, which are both specifically designed for training and inference, respectively, at costs up to 50% lower than comparable GPUs. Added Elastic Fabric Adapter (EFA) to enable high-performance computing (HPC)-type networking between EC2 instances for distributed training.

Google Cloud offers TPU v5p pods with over 8,960 chips, used for training large language models. Google DeepMind and Anthropic are among the first to use them. Built A3 Mega VMs featuring NVIDIA H100 GPUs, 3.6 TB of system memory, and 3.6 TB/s bandwidth and which are AI training-optimized.

Microsoft Azure is equipping ND H100 v5-series VMs with NVIDIA H100 GPUs and Quantum-2 InfiniBand interconnects to train models

2. Managed AI Platforms

Allowing developers to develop, deploy, and execute ML models with auto-scaling endpoints, model versioning, feature stores, and MLOps capabilities.

AWS SageMaker Supports distributed training on hundreds of GPU instances, model parallelism, spot training, and multiple model endpoints for cost savings.

Azure Machine Learning includes AutoML, prompt engineering, and GitHub Copilot integration for AI life cycle management.

3. Advanced Storage and Data Fabric

Databricks and AWS Lake Formation are enabling AI training on large data lakes using Delta Lake and Apache Iceberg.  Google Cloud BigLake and Unified Storage offerings will combine data lakes and warehouses into a single engine ideal for model training.

4. Federated and Edge AI Support

AWS Greengrass and Google Edge TPU allow real-time inference on the edge devices with lower latency and data transfer expenses. Microsoft Azure Stack Edge brings GPU-accelerated inference to field, factory, and hospital environments.

Analogue Artificial Intelligence(Opens in a new browser tab)

Cloud providers are answering the challenge with a new generation of infrastructure, platforms, and developer tools natively architected for AI at scale. The victors in the struggle will be those that also provide, alongside access to raw compute, flexibility, cost-effectiveness, sustainability, and ease of use for developers and enterprises. As AI propels the wave of innovation to come, the cloud must grow not in size alone, but in intelligence, flexibility, and trust.