With AI Cloud, Will Intel Compete with AWS, Azure and Google Cloud?

Intel is wading in troubled waters. Yet, in recent months, it has made several attempts to reposition itself in the market by launching new products and exploring strategic partnerships to enhance its competitiveness.

Recently, Intel announced an AI cloud service, Tiber AI Cloud, powered by its new Gaudi 3 accelerator chips. The new offering is designed for enterprises and AI startups looking to leverage powerful cloud resources for scalable AI development and deployment.

However, this, in turn, will put Intel in direct competition with hyperscalers such as Amazon Web Services (AWS), Microsoft Azure and Google Cloud. Interestingly, these hyperscalers are among the biggest customers of NVIDIA, the company known for its advanced GPUs that Gaudi 3 is being positioned against.

Competing with Hyperscalers?

So, is Intel aiming to compete with the hyperscalers? Not exactly. Intel does not intend to become the next hyperscaler or establish data centres across the globe. Instead, its focus is solely on the AI cloud space, where it will compete with the hyperscalers.

This pivot may also reflect Intel’s efforts to explore new business opportunities during a challenging period. This year, the company’s stock has dropped over 60%; however, Intel has said that currently, their focus is on AI compute only.

Instead, the pivot to enterprise AI cloud has come from growing demand from customers. Markus Flierl, corporate vice president, developer cloud, has told CRN that Tiber AI Cloud is a response to the growing demand from customers.

Intel said besides Gaudi 3, Intel will provide Gaudi 2 chips, Xeon CPUs, Core Ultra 200V CPUs to its customers as a part of its AI cloud offerings.

For some time now, Intel has asserted that for most enterprise AI use cases that don’t involve training large language models, high-end, expensive GPUs are unnecessary, and a robust stack of CPUs can be sufficient.

However, the hyperscalers have stacked their data centres with high-end NVIDIA GPUs, and at this moment, it seems like this is what most customers want.

While NVIDIA does continue to dominate the market, it does not mean there are no takers for Intel’s AI chips. For instance, Bhavish Aggarwal’s Krutrim—India’s first generative AI unicorn—has leveraged Intel’s Gaudi 2 chips to train its AI models.

Similarly, other Indian companies such as IT giant Tech Mahindra and Infosys have announced a partnership with Intel to use their hardware for AI. Recently, Inflection AI also announced a partnership with Intel to launch a new enterprise Al system called Inflection for Enterprise. 

Gaudi 3 vs NVIDIA

AI cloud also seems like a good strategy to get Gaudi 3 out in the market and get customers leveraging them. Intel’s Gaudi AI accelerators are seen as a challenger to NVIDIA’s dominance in the AI chip market. 

For Intel, convincing AI companies to switch from NVIDIA GPUs to Gaudi 3 for model training may be a hard sell. Therefore, an AI cloud solution seems like a logical move.

Launched earlier this year, Gaudi 3 represents Intel’s ambitious push into the rapidly growing AI computing space. According to Intel, Gaudi 3 can deliver up to 70% faster training times for large language models like Llama 2 and GPT-3. 

For inference tasks, Gaudi 3 is said to match or outperform the memory-rich H200 in certain scenarios, particularly with larger output sequences.

Many enterprises and AI startups do not possess the resources to acquire these high-end NVIDIA GPUs. Given the constraint in resources, they look for the most cost-effective solutions and Gaudi 3 chips are relatively cheaper compared to NVIDIA’s H100 GPUs.

Moreover, with Tiber AI Cloud, Intel is likely to begin renting out its AI chips at an hourly rate. To attract many startups and enterprises, the company will need to offer its AI chips at a lower price than the hyperscalers, making it an appealing option.

For instance, Indian AI cloud companies like E2E Networks and Yotta offer NVIDIA’s H100 GPUs at a competitive rate of approximately INR 400-500 per hour, making these GPUs accessible to Indian enterprises and startups.

Stabilising a Rocking Ship?

Intel has been trying to catch up to NVIDIA for a considerable time, after failing to capitalise early on the surge in AI-specific chips, like the latter did.

Additionally, Intel has encountered substantial delays and challenges in its chip manufacturing processes, enabling rivals like TSMC to gain an advantage in advanced chip production. Notably, Gaudi 3 is reportedly based on TSMC’s 5 nm node.

Reports from last month also suggested that Broadcom is in talks to acquire Intel, or at least, a part of it. In Q2 2024, Intel also reported a $1.6 billion loss.

With Gaudi 3, Intel hopes to bring some respite to the company and help steady the boat. However, NVIDIA is not the only company Intel is competing with.

In recent years, a wave of AI chip startups has emerged, creating chips that, in many cases, outshine NVIDIA’s high-end GPUs.

For inference tasks, D-Matrix, a startup founded by Sid Sheth, is developing silicon which works best at inferencing tasks. Its flagship product, Corsair, is specifically designed for inferencing generative AI models (100 billion parameter or less) and is much more cost-effective, compared to GPUs.

Groq, another AI chip startup, founded by Jonathan Ross in 2016, claims their AI chips are ten times faster, ten times cheaper, and consume ten times less power.

While challenges persist, it remains to be seen whether Intel’s AI cloud will achieve broader adoption and how Gaudi 3 chips will perform in comparison to NVIDIA.

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