Liftr data introduces a new data service for market intelligence about AI Training and AI Inference semiconductors.
Liftr Insights, a pioneer in market intelligence driven by unique data, launched its Liftr Intelligence Compute TrackerSM data service.
Since the explosive interest in ChatGPT by OpenAI, the demand for AI chips, especially the NVIDIA Hopper H100, has skyrocketed, but the impact extends to chips by Intel, AMD, and cloud service providers like AWS and Google, themselves.
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Liftr Insights adds new AI Semiconductor data service with 5 years of history
Liftr announced the Liftr Intelligence Compute Tracker to be hyper-focused on hyperscale AI for market intelligence analysts. This new data set tracks the pulse of AI introductions, directional trends, market share, underlying product details, and insights into the who, what, and where of AI infrastructure.
“Among all the hype and speculation, Liftr data—especially our Intelligence Compute Tracker data—provides objective insights into AI activity. Like our other offerings, this data is a regular delivery of reliable, objective data.” – TAB SCHADT, CEO of Liftr Insights
Liftr Insights cultivated the Intelligence Compute Tracker to focus on data that is driving AI. The data leverages a subset of the Liftr Cloud Components TrackerSM, which Liftr has offered to corporate and investment clients for the past 5 years. The Intelligence Compute Tracker service expands the data set with supplemental objective data that provides additional insight into infrastructure components that are not fully deployed or have limitations on usage.
CoreWeave and Lamdba, among others, will be included in the data set. Both companies received attention for their early deployment of NVIDIA GPUs, especially the H100.
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In the coming months, market intelligence analysts will be focused on the arrival of many more chips designed for AI Training and AI Inference. Analysts are watching for the NVIDIA H400, AMD MI300, and the Intel Gaudi 3. The data set also includes accelerators unique to cloud providers such as Google TPUs and AWS Inferentia. This new data set will track when and where these instances appear and where they succeed (or don’t).
Historical trends are a critical measure for evaluating current trend rates. The Liftr Intelligence Compute Tracker fully tracks (not just samples) over 75% of the public cloud data with 5 years of history. For example, Liftr customers can see important correlations between the growth of H100’s compared to the A100s. That historical trend can help refine projection models.
“It’s not just a matter of when we first see accelerators appear,” says Schadt. “The more interesting question is what happens after they appear. Do they grow? Flatten out? These all provide insights for designing new products or marketing existing offerings.”
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