Navigating the Cloud GPU Landscape

We see AI adoption and GPU usage as the next wave of cloud adoption and transformation. As a result, the cloud GPU market has evolved into a complex ecosystem of providers, each offering unique solutions across different performance tiers. Our analysis has focused on Nvidia technology and divided the GPUs into four tiers. 

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H100 Tier – Premium Performance

The NVIDIA H100 Tensor Core GPU represents the pinnacle of computational power in AI, high-performance computing (HPC), and data centre applications. 

Technical Features

The H100 GPU is built on the NVIDIA Hopper architecture, which introduces several groundbreaking features:

• Fourth-Generation Tensor Cores: These cores deliver up to 6x faster chip-to-chip communication than the previous A100 model. On a per-SM basis, they provide 2x the Matrix Multiply-Accumulate (MMA) computational rates for equivalent data types and 4x the rate using the new FP8 data type. This leap in performance is crucial for AI and HPC workloads, enabling faster training and inference of complex models.
• Transformer Engine: Specifically designed for handling trillion-parameter language models, the Transformer Engine accelerates AI training and inference by up to 9x and 30x, respectively, compared to the A100. This feature is pivotal for applications in natural language processing, computer vision, and other AI-driven tasks.

Scalability and Interconnects

• NVLink Switch System: The H100 can connect up to 256 GPUs across multiple compute nodes, facilitating model parallelism for the most challenging computing tasks. This system provides a total bandwidth of 900 GB/s, significantly enhancing the scalability of AI and HPC workloads.
• PCIe Gen5: With the introduction of PCIe Gen5, the H100 offers improved interconnect bandwidth, ensuring that data transfer between the GPU and other system components is as fast as possible.

Memory and Bandwidth

• HBM3 Memory: The H100 features 80GB of HBM3 memory, which provides a bandwidth of up to 3.35TB/s. This bandwidth is essential for handling large datasets and complex simulations without bottlenecks.

Security and Confidential Computing

• Confidential Computing: The H100 is the first GPU to support confidential computing. It isolates workloads in virtual machines (VMs) to enhance security in multi-tenant environments. This feature is vital for processing sensitive data in AI training or inference.

Use Cases

The H100’s capabilities have been demonstrated in various high-impact applications:

• Supercomputing: It has significantly boosted the performance of supercomputers, contributing to over 2.5 exaflops of HPC performance across leading systems.
• AI Research: The H100 has transformed AI research and application development by accelerating the development of large language models and enabling real-time analytics.
• Pharmaceutical Research: The NVIDIA DGX H100 system, powered by H100 GPUs, is utilised by the Centre for Continuous Manufacturing and Crystallisation (CMAC) to drive AI models for drug development and manufacturing, showcasing its potential in life sciences.

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A100 Tier – Enterprise Grade

The NVIDIA A100 GPU offers enterprise-grade computing power, balancing performance, reliability, and scalability for organisations requiring production-ready AI and HPC capabilities.

Technical Specifications

The A100 boasts impressive hardware specifications that make it suitable for enterprise deployments:

• 40GB or 80GB HBM2e memory configurations
• Memory bandwidth of 2,039GB/s
• 6,912 NVIDIA Ampere Architecture-Based CUDA cores
• 312 TFLOPS for TF32 operations

Enterprise Features

Multi-Instance GPU (MIG)

MIG technology enables enterprises to partition a single A100 GPU into up to seven isolated instances, each with dedicated memory, cache, and compute cores. 

This feature ensures GPU utilisation and guarantees Quality of Service for multi-tenant environments.

Security and Reliability

The A100 includes enterprise-grade security features such as:

• NEBS Level 3 certification
• Secure Boot capabilities
• Hardware-level isolation for workloads

Performance Scaling

The A100 delivers significant performance improvements for enterprise workloads:

• Up to 20X higher performance compared to the previous Volta generation
• 1.7X higher memory bandwidth over the previous generation
• Double the data transfer speeds with PCIe Gen 4 support

Use Cases

Financial Services

Financial institutions can leverage the A100 for risk analysis, algorithmic trading, and large-scale data processing.

Healthcare and Life Sciences

The platform enables breakthrough research in drug discovery, genomic analysis, and personalised medicine development.

Technology and IT Services

Cloud providers and data centres can offer enhanced services with the following:

• High-performance computing capabilities
• Accelerated AI workloads
• Improved infrastructure efficiency

Cloud Integration

The A100 serves as a foundation for enterprise cloud computing, enabling:

• Elastic resource allocation
• Dynamic workload adjustment
• Efficient scaling of AI and analytics applications

Cost Efficiency

For enterprises, the A100 provides significant operational benefits:

• Improved throughput for large-scale workloads
• Reduced data centre costs through efficient resource utilisation
• Enhanced performance per watt with 400W standard configuration
• The A100 represents a mature, enterprise-grade solution that combines performance, reliability, and scalability for organisations requiring production-ready AI and HPC capabilities.

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V100 Tier – Reliable Workhorse

The NVIDIA V100 GPU continues to serve as a dependable foundation for AI and HPC workloads, earning its reputation as the data centre’s reliable workhorse.

Technical Specifications

The V100’s fundamental specifications demonstrate its enduring value:

• 16GB or 32GB HBM2 memory configurations
• 900GB/s memory bandwidth
• 5,120 NVIDIA Volta Architecture CUDA cores
• 640 Tensor Cores

Proven Architecture

The Volta architecture has demonstrated exceptional reliability in production environments, making it ideal for:

• Long-running production workloads
• Consistent performance delivery
• Stable operation in diverse computing environments

Resource Management

The V100 provides reliable resource utilisation through:

• Predictable performance characteristics
• Mature driver support
• Well-documented optimisation techniques

Use Cases

Machine Learning Operations

The V100 excels in production ML environments:

• Training of established model architectures
• Inference deployment at scale
• Batch processing operations

Scientific Computing

Research institutions continue to rely on V100s for:

• Physics simulations
• Climate modelling
• Molecular dynamics calculations

Cost-Performance Balance

The V100 offers several economic advantages:

• Lower acquisition costs compared to newer generations
• Proven ROI for established workloads
• Extensive ecosystem compatibility

While newer GPU generations offer higher peak performance, the V100 is dependable for organisations requiring proven reliability and consistent performance. 

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RTX Tier – Development and Testing

The RTX tier represents an accessible entry point for AI and ML development. It offers capabilities well-suited for development, testing, and smaller production workloads.

Technical Specifications

RTX 4090

• 24GB GDDR6X memory
• 384-bit memory bus
• 82.6 TFlops FP32 compute power
• 1.29 TFlops FP64 compute power
• 1,008 GB/s memory bandwidth
• 450W TDP

RTX 4080

• 16GB GDDR6X memory
• 256-bit memory bus
• 48.7 TFlops FP32 compute power
• 0.76 TFlops FP64 compute power
• 717 GB/s memory bandwidth
• 320W TDP

RTX 3090

• 24GB GDDR6X memory
• 384-bit memory bus
• 936 GB/s memory bandwidth
• 350W TDP
• 10,496 CUDA cores

RTX 3080

• 10GB GDDR6X memory
• 320-bit memory bus
• 760 GB/s memory bandwidth
• 320W TDP
• 8,704 CUDA cores

Use Cases

Development Workflows

RTX GPUs excel in development scenarios:

• Model prototyping
• Code testing and validation
• Small-scale inference deployments

Educational and Research

These GPUs are particularly valuable for:

• Academic research projects
• Learning environments
• Proof-of-concept development

Cost Benefits

RTX solutions offer significant advantages for development:

• Lower hourly rates compared to enterprise tiers
• Pay-as-you-go pricing models
• Reduced costs for development and testing phases

The RTX tier is an ideal platform for development and testing environments, offering a balance of performance and cost-effectiveness. While not designed for large-scale production workloads, it provides the necessary capabilities for developers to build, test, and validate their AI and ML applications before moving to more powerful tiers for production deployment.

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Service Model Differentiation

The market organises itself into three distinct service models:

Serverless Solutions

• Minimal infrastructure management
• Pay-per-use pricing
• Ideal for variable workloads

VM-Based Services

• Traditional cloud computing model
• Greater control over resources
• Flexible scaling options

Bare Metal Offerings

• Maximum performance
• Direct hardware access
• Suitable for specialised workloads

Big Tech Integration

Major cloud providers maintain a significant presence:

• AWS
• Google Cloud
• IBM Cloud
• Microsoft Azure
• NVIDIA DGX Cloud
• Oracle Cloud

These providers offer integrated solutions that combine GPU resources with their broader cloud ecosystems.

Non-NVIDIA Alternatives

The market also includes non-NVIDIA solutions, with providers like Fasthosts and Hivelocity offering alternative GPU architectures. This diversity provides options for organisations with specific hardware requirements or those seeking cost-effective alternatives.

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Strategic Considerations

When selecting a GPU cloud provider, consider:

1. Workload characteristics and performance requirements

2. Integration needs with existing infrastructure

3. Budget constraints and pricing models

4. Geographic availability and data sovereignty

5. Support requirements and service level agreements

The cloud GPU landscape continues to evolve, offering increasingly specialised solutions for diverse computing needs. Understanding this ecosystem is crucial for making informed decisions that align with your organisation’s technical and business objectives.