Intel Modestly Lays its Case for AI
Today at #AIFD4 is Intel Day! Color me excited to hear about how the folks at Intel are pursuing AI workloads in 2024 given the acute innovation across the silicon arena in this space. Disclaimer: for readers unaware of my background, I spent 22 years at Intel including ownership of Xeon and AI marketing so I have a special place in my heart for this groundbreaking platform and its role in cloud to edge performance. And I don’t think it’s a surprise to anyone that Intel has been taking it on the chin in the AI arena over the past few years. They’ve at lost core CPU performance leadership to rival AMD, while NVIDIA at the same time has claimed AI silicon supremacy with their GPUs delivering the training required to unleash LLMs like Chat GPT.
Revenues and profit have suffered, and many are talking about the era of CPU centric computing coming to an end in the era of AI. At the TechArena, we’ve written extensively about the budding silicon industry developing GPU alternatives for both AI training and inference and across model sizes, power constraints, and locations from cloud to edge. So…what is Intel doing to right the ship?
Today Ro Shah, Xeon AI Director at Intel, led the charge with a discussion of where CPUs perform best and where accelerators (assuming including Intel’s own offerings) would be better tools for the job. He described a moat of 20B+ size of LLM as the magical divide between this CPU accelerator decision and outlined the evolution of AI acceleration embedded on Xeon processors over the years including the most recent integration of Intel AMX technology for INT8 and BF16. The result was a shockingly modest positioning coming from the market leader on where CPU will perform. Many of the delegates responded to this modesty with follow up questions…because there must be more right? With accelerators being developed in the “CPU zone” and targeting power sipping TDPs, we are left wondering about the magical divide being an accurate depiction of the market and where it’s going. And does it leave some of the real value that is Intel on the table?
The world today still largely runs on x86, and IT departments are ridiculously comfortable deploying Xeon processors, even today. That’s what you get when you’ve delivered consistently in market for decades. As we see the proliferation of AI extend from the sophistication of large cloud environments, and the engineers who live there that build AI models at breakfast, to the mid-market, organizations need an easy button for AI inference and Intel is uniquely poised to offer this to customers. They’ve delivered the investment in integrated AI acceleration and tools like OpenVINO to get initial AI solutions off the ground, and in so doing garner continued customer loyalty. They are also delivering Intel Core platforms for client and winning the perception game of AI optimization on these platforms which are integrating more and more inference over time.
I would like to see them claim more here, and I’m looking forward to seeing more from them as they continue to deliver both advancements in the core CPU and acceleration elements of their portfolio. I’m also excited to hear more from the other players in the industry on how accelerators will target the inference arena, how NVIDIA will leverage GPU supremacy for broader workload targets, and how AMD will leverage its performance to gain ground in broad market deployments. 2024 is going to be an exciting ride! Watch this space.
Today at #AIFD4 is Intel Day! Color me excited to hear about how the folks at Intel are pursuing AI workloads in 2024 given the acute innovation across the silicon arena in this space. Disclaimer: for readers unaware of my background, I spent 22 years at Intel including ownership of Xeon and AI marketing so I have a special place in my heart for this groundbreaking platform and its role in cloud to edge performance. And I don’t think it’s a surprise to anyone that Intel has been taking it on the chin in the AI arena over the past few years. They’ve at lost core CPU performance leadership to rival AMD, while NVIDIA at the same time has claimed AI silicon supremacy with their GPUs delivering the training required to unleash LLMs like Chat GPT.
Revenues and profit have suffered, and many are talking about the era of CPU centric computing coming to an end in the era of AI. At the TechArena, we’ve written extensively about the budding silicon industry developing GPU alternatives for both AI training and inference and across model sizes, power constraints, and locations from cloud to edge. So…what is Intel doing to right the ship?
Today Ro Shah, Xeon AI Director at Intel, led the charge with a discussion of where CPUs perform best and where accelerators (assuming including Intel’s own offerings) would be better tools for the job. He described a moat of 20B+ size of LLM as the magical divide between this CPU accelerator decision and outlined the evolution of AI acceleration embedded on Xeon processors over the years including the most recent integration of Intel AMX technology for INT8 and BF16. The result was a shockingly modest positioning coming from the market leader on where CPU will perform. Many of the delegates responded to this modesty with follow up questions…because there must be more right? With accelerators being developed in the “CPU zone” and targeting power sipping TDPs, we are left wondering about the magical divide being an accurate depiction of the market and where it’s going. And does it leave some of the real value that is Intel on the table?
The world today still largely runs on x86, and IT departments are ridiculously comfortable deploying Xeon processors, even today. That’s what you get when you’ve delivered consistently in market for decades. As we see the proliferation of AI extend from the sophistication of large cloud environments, and the engineers who live there that build AI models at breakfast, to the mid-market, organizations need an easy button for AI inference and Intel is uniquely poised to offer this to customers. They’ve delivered the investment in integrated AI acceleration and tools like OpenVINO to get initial AI solutions off the ground, and in so doing garner continued customer loyalty. They are also delivering Intel Core platforms for client and winning the perception game of AI optimization on these platforms which are integrating more and more inference over time.
I would like to see them claim more here, and I’m looking forward to seeing more from them as they continue to deliver both advancements in the core CPU and acceleration elements of their portfolio. I’m also excited to hear more from the other players in the industry on how accelerators will target the inference arena, how NVIDIA will leverage GPU supremacy for broader workload targets, and how AMD will leverage its performance to gain ground in broad market deployments. 2024 is going to be an exciting ride! Watch this space.
