31 Latest Generative AI Infrastructure Statistics in 2024

Generative artificial intelligence (AI) infrastructure makes it easier to develop and deploy scalable generative models. They combine natural language understanding and machine learning (ML) technologies to help organizations proactively create efficient, scalable, and secure training environments.

Many companies use Generative artificial intelligence infrastructure software Overcome model scalability challenges while promoting high inference speed and usability.essential to use Large Language Model (LLM) and other generative artificial intelligence technologies.

Here are some statistics about the state of generative AI infrastructure in 2024.

Generative AI Infrastructure Statistics

These statistics demonstrate how companies are using and increasing their adoption of generative AI infrastructure. Find out which frameworks professionals prefer for model customization.

• AI servers are expected to generate $132 billion in hardware sales.
• A survey of 50 technology companies showed that 64% planned to adopt generative artificial intelligence technology.
• The artificial intelligence infrastructure market is worth US$23.5 billion in 2021 and is expected to surge to US$309.4 billion by 2031, with a compound annual growth rate (CAGR) of 29.8% from 2022 to 2031.
• Widespread adoption of open source frameworks for model customization shows high levels of satisfaction. This demonstrates that the flexibility of AI infrastructure is critical to meeting growing demand. More than 78% of respondents are satisfied or very satisfied with their current solutions, indicating that open source frameworks provide respondents with what they need.

• The top ways companies maximize graphics processing unit (GPU) utilization include queue management and job scheduling (67%), multi-instance GPU provisioning (39%) and setting usage quotas (34%).
• Users’ techniques for optimizing GPU allocation vary. 24% use open source solutions, 27% use high-performance computing (HPC) solutions, and 34% use vendor-specific solutions. In addition, 11% rely on Microsoft Excel and 5% rely on customized solutions.
• To monitor GPU utilization, 36% of companies use Google Cloud Platform-GPU metrics as the primary method, followed by 30% using NVIDIA AI Enterprise.
• Other tools such as IBM Load Sharing Facility (LSF) and Kubernetes were used by 15% and 13% of respondents.

Top Generating Artificial Intelligence Growth and Adoption Statistics

These statistics show the overall growth of artificial intelligence and how people perceive it. Understanding these statistics will help you evaluate upcoming opportunities in the industry as well as possible infrastructure needs.

Use these data points to understand what people really think about artificial intelligence. Learn how men use artificial intelligence differently than women or children.

• In 2022, the generative AI market will be valued at US$29 billion.
• The generative AI market is expected to be worth more than $66 billion by the end of 2024.
• One report predicts that the generative artificial intelligence market may reach a staggering US$1.3 trillion by 2032.
• North America dominates generative AI revenue, accounting for 40.2% of the global share, mainly due to the presence of major technology companies such as Microsoft, OpenAI, Meta, Adobe, IBM, and Google.

• Use of AI varies, with 44% of companies using it for cloud pricing optimization and 41% using it for voice assistants and chatbots.
• A survey of 821 companies shows that through investment in generative artificial intelligence, costs may be reduced by 15.7% in the next 12 to 18 months.
• Chatbots save an average of 2 hours and 20 minutes per day, while generative AI in customer service response writing saves businesses approximately 2 hours and 11 minutes per day.
• Men are twice as likely as women to use generative AI, with significant differences in usage of platforms such as ChatGPT, which had an average of 1.5 billion monthly visits in 2023.
• Regarding children’s use of artificial intelligence chatbots such as ChatGPT, 31% of men and only 4% of women are willing to allow children to use these technologies for any purpose.
• The marketing and advertising industry leads the adoption of generative AI, even surpassing the technology industry, with adoption rates of 35% and 30% respectively in consulting, 19% in teaching, and 16% in accounting. In healthcare it is 15%.

Concerns about generative AI infrastructure and systems

As interest in artificial intelligence technology grows, some organizations are deeply concerned about its impact on security. Some companies are concerned about its cost and computing limitations.

• 58% of organizations have yet to adopt artificial intelligence due to cybersecurity concerns.
• Key cost drivers for generative AI include integration and GPU expenses for model development and training. Despite this, 56.8% of companies expect double-digit revenue growth from AI/ML investments and AI transformation in 2024.
• Companies are actively seeking cost-effective GPU alternatives for AI inference to manage computing constraints, which remains a top challenge.
• When managing GPU resources, companies use a variety of strategies, including queue management and job scheduling, with 78% using more than half of their GPU resources during peak hours.

• 74% of survey respondents believe it would be beneficial to integrate computers and scheduling into one platform. This integration supports faster, more efficient model development and deployment.
• As companies plan to meet higher computing demands in 2024 and plan to use LLM in production, executives are weighing current challenges against future needs, especially given the scarcity of GPUs for inference tasks.
• Model Service supports access to machine learning models through application programming interfaces (APIs), which is critical for artificial intelligence integration applications. About one-third of companies do not have model serving capabilities, which are becoming increasingly necessary due to the increasing performance requirements of generative AI models.
• 61% of survey participants were somewhat dissatisfied with their current scheduling tools, and 12% felt neutral, indicating significant potential for improvement.
• Key issues with current tools include insufficient GPU optimization (53%) and user-friendliness for developers and data scientists (47%). Additionally, approximately 25% of respondents reported control and compatibility issues with existing AI/ML stacks.

The future of generative artificial intelligence

The future of artificial intelligence seems bright and promising, with several companies planning to expand their artificial intelligence and automation capabilities. The statistics below reflect this.

• Nearly all companies surveyed (96%) plan to expand their AI computing capabilities, focusing on cloud solutions due to their flexibility and speed despite concerns about waste and idle costs.

• Between 5% and 10% of companies have begun integrating generative AI into their production processes.

This is the technological future driven by artificial intelligence

As more companies build and expand the deployment of AI systems in their operations, the demand for AI infrastructure will rise. Currently, there are some concerns related to cost and safety. However, as technology advances, these concerns may turn into business opportunities that leaders need to address.

Learn more about trends AI and future projections.