Development Trends in GPU Cloud Access Technologies Amid the Rise of LLM and GenAI (pre-order)

Development Trends in GPU Cloud Access Technologies Amid the Rise of LLM and GenAI (pre-order)

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. 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.

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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
6. MIC Perspective

Appendix
List of Companies

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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

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