Ensuring Infrastructure for Generative AI Integration: Navigating the Chip & Cloud war

The AI chip market is forecasted to exceed $140 billion by 2027, more than doubling its current size, according to Gartner's projections. This is largely fuelled by Generative AI, which is increasingly captivating the business world, gaining momentum as tech giants like Google, Microsoft, Amazon, and others introduce their own offerings to the market.

For businesses considering integrating Generative AI into their operations and aiming to reap its benefits, it is crucial to consider the infrastructure that is use to support it.

A key debate in the Generative AI world revolves around Chip vs Cloud for Generative AI capabilities, between on-device processing with specialised chips or utilising cloud-based solutions.

Businesses need to weigh multiple factors to make an informed decision. Cloud-based AI provides scalability, flexibility, and access to extensive data for model training, all while avoiding costly hardware investments. However, chip-based Generative AI offers quicker processing speeds, reduced latency, and centralised security by processing data locally.

Although cloud-based solutions currently appear to be the more convenient choice for many businesses, it is crucial to anticipate future trends, individual requirements, budget, data privacy concerns, and the desired level of control over Generative AI models.

Want to learn more about laying the foundations for Generative AI? Generative AI Summit 2024 will feature 4 sessions on infrastructure, with speakers from Spotify, GlaxoSmithKline, Pfizer.

The Chip vs. Cloud Debate

What is chip-based Generative AI?

Chip-based Generative AI involves artificial intelligence systems that leverage dedicated chips to independently generate new content. These systems can produce text, images, music, and various media types by drawing on patterns and data from their training. By utilising specialised hardware designed for AI tasks, chip-based Generative AI can produce quicker and more effective outcomes than more conventional cloud-based methods.

Among the different types of AI chips, graphics processing units (GPUs) are the most frequently utilised, with NVIDIA leading as the primary producer. Recently, Microsoft, Google, and Amazon have also ventured into the chip-based market.

Unfortunately, there has been a consistent scarcity of powerful chips in recent years, which are boosting costs and hampering supply chains.

AI chips at a glance:

• Cost: AI-based chips are more expensive, complex, and difficult to produce compared to traditional GPUs and CPUs due to technical requirements.
• Complexity: The latest generation of Generative AI systems relies on state-of-the-art AI chips to avoid high energy consumption costs associated with older chips and GPUs.
• Supply chain: Shortage of AI chips is a major bottleneck for the AI industry and a potential risk for investors.
• Audience: AI hardware plays a crucial role in training and operating AI models, posing challenges for companies developing large language models and their customers using Generative AI applications.
• A growing market: AWS, Microsoft, and Google have recently introduced their own AI-capable chips. Selling these chips to enterprise companies in the future may help reduce costs, resolve chip shortages, and potentially offer chip access to companies utilising their cloud services.

What cloud-based Generative AI?

Cloud-based Generative AI operates by sending data to remote servers for processing and content generation. This approach allows for more complex computations and larger datasets to be used, but it can introduce latency and potential privacy concerns since data is being transmitted over the network.

Cloud at Glance

• Scalability: Cloud platforms enable organisations to scale Generative AI workloads as needed, which is crucial for projects with large datasets.
• Implementation: Companies can focus more on integrating Generative AI into their products and services rather than managing the underlying hardware.
• Collaboration: Cloud-based platforms allow teams to collaborate on Generative AI projects regardless of their physical location.
• Security: Cloud providers place a high priority on security and compliance. Yet, concerns persist regarding digital sovereignty, maintaining control over data storage and operational locations, as well as ensuring sustainability for implementing Generative AI.
• Cost: Accessing AI-specific processors through cloud service providers eliminates the need for very costly initial investments.

Learn more at the upcoming Generative AI Summit 2024, featuring sessions on generative AI infrastructure such as “Advancing Generative AI Through Robust Infrastructure Support”.

Navigating the Infrastructure Challenge

The chip landscape

In light of the cost, and general supply chain issues of AI chips, companies will likely remain cautious jumping into chip and hardware-based solutions. Large organisations at the enterprise level seeking AI chips for training extensive language models might face delays in acquiring these chips, which could impact their ability to manage heavier workloads effectively.

However, prices of next-generation AI accelerator chips are expected to decrease within the next few years aided by diversified supply and lower than predicted AI chip demand. This potential decline in price and increase in supply may lead to the introduction of new suppliers in the market offering chip-based solutions. In the short term, strategies for navigating a chip-based approach should consider both the use of AI chips and cloud services to optimise performance and cost-effectiveness.

The cloud landscape

The cloud's core appeal lies in its accessibility, as cloud platforms grant a wider audience access to high-performance computing resources. Generative AI models often demand significant computational power, which can be cost-prohibitive for many organisations to implement effectively. On the other hand, cloud providers offer managed Generative AI services that simplify the complexities of training and deploying Generative AI models, besides providing pre-built solutions.

The majority of companies are expected to rely on cloud providers for Generative AI solutions, with fewer opting for hardware investments. But as always, various obstacles like regulations, privacy concerns, and accuracy challenges could impede widespread adoption of generalised AI in enterprise software.

Paving the Way for Generative AI Integration

While cloud-based AI offers flexibility and accessibility, chip-based AI provides faster processing speeds and localised security. Ultimately, the choice between the two options will depend on factors such as budget constraints, data sensitivity, and the need for real-time processing capabilities.

For many companies, cloud proves to be the most practical choice for accessing generative AI. In consideration of the ongoing chip shortage and high initial costs, a hardware solution will likely be limited to larger corporations capable of developing in-house solutions .However, with new suppliers entering the market that may sell AI-enabled chips at a reduced cost, the landscape and bar of entry may shift in the future.

What next?

If you want to keep on top of the Generative AI landscape to understand how you can implement Generative AI? Register to attend Generative AI Summit 2024. This conference is the strategic, practical hub for leaders in AI, Data, Technology, and Innovation sectors, focusing on the transition from initial Generative AI trials to robust, enterprise-wide applications that deliver real value. Themes include:

• Industry Disruption: With discussions successfully navigating business model shifts, learn how to capitalise on the transformative potential of AI, gaining a competitive edge in the rapidly evolving landscape.
• Cutting-Edge Use Cases: Delve into a diverse array of real-world practical applications through raw use cases showcasing how Generative AI is revolutionising industries, and any challenges faced along the way.
• Ethical AI Frameworks: Emphasis on establishing responsible AI practices, ensuring ethical considerations are at the core of your AI initiatives.

Return to Home