This report provides an overview of GPU cloud services, examining the development of local GPU cloud access technologies - such as private cloud and consumption-based pricing models - traditional remote GPU cloud access technologies, including virtual machine and bare-metal-as-a-service (BMaaS) technologies, and emerging remote GPU cloud access technologies, such as container and serverless architectures. A comparative analysis of these six GPU cloud access technologies is also presented.
In recent years, the global surge in applications for Large Language Models (LLMs) and Generative AI (GenAI) has driven major cloud service providers to make substantial investments in graphic processing units (GPUs) to accelerate AI computations. With chip supply constraints expected to persist in the short to medium term, users are increasingly turning to GPU cloud services to support their AI applications. However, given the diversity of access technologies available for these services, users must conduct thorough evaluations to make informed decisions.
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Table of Contents
1. Background of GPU Cloud Services
1.1 Rise of Large Language Models (LLM)
1.2 Transition in Cryptocurrency Mining Services
2. Local GPU Cloud Access Technologies
2.1 Private Cloud
2.2 Consumption-based Pricing
3. Traditional Remote GPU Cloud Access Technology
3.1 Virtual Machine
3.2 Bare Metal-as-a-Service (BMaaS)
4. Emerging Remote GPU Cloud Access Technology
4.1 Container
4.2 Serverless
5. Comparative Analysis
Appendix
List of Companies
List of Tables
Table 1: Comparison of Six GPU Cloud Access Technologies
List of Figures
Figure 1: Mining Service Providers Consider Transformation After Bitcoin's Significant Drop in 2022
Figure 2: HPE GreenLake Offers Users Server Access with Base and Usage Fees, Instead of One-Time Purchases
Figure 3: Bare Metal Servers Allocate More Resources to Computation by Skipping VM Hypervisors and Container Engines
Figure 4: Nvidia Introduces NIM at Computex 2024
Figure 5: Serverless Computing Minimizes Idle Resources through Automatic Activation and Deactivation
Companies Mentioned (Partial List)
A selection of companies mentioned in this report includes, but is not limited to:
- Amazon
- AWS
- Banana Dev
- Baseten
- Bit Digital
- CoreWeave
- Dell
- Fal AI
- Hive
- HPE
- Hut 8
- IBM
- Lenovo
- Microsoft
- Midjourney
- Modal Labs
- Nvidia
- Open AI
- Oracle
- Replicate
- RunPod
Methodology
Primary research with a holistic, cross-domain approach
The exhaustive primary research methods are central to the value that the analyst delivers. A combination of questionnaires and on-site visits to the major manufacturers provides a first view of the latest data and trends. Information is subsequently validated by interviews with the manufacturers' suppliers and customers, covering a holistic industry value chain. This process is backed up by a cross-domain team-based approach, creating an interlaced network across numerous interrelated components and system-level devices to ensure statistical integrity and provide in-depth insight.
Complementing primary research is a running database and secondary research of industry and market information. Dedicated research into the macro-environmental trends shaping the ICT industry also allows the analyst to forecast future development trends and generate foresight perspectives. With more than 20 years of experience and endeavors in research, the methods and methodologies include:
Method
- Component supplier interviews
- System supplier interviews
- User interviews
- Channel interviews
- IPO interviews
- Focus groups
- Consumer surveys
- Production databases
- Financial data
- Custom databases
Methodology
- Technology forecasting and assessment
- Product assessment and selection
- Product life cycles
- Added value analysis
- Market trends
- Scenario analysis
- Competitor analysis
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