???June 7x7
??Hi! Your favorite source for everything efficient AI-related, Pompom is here!
June has been a month packed with events and achievements. We pitched at the Demo Day on Google’s Mountain View campus, wrapping up the 10-week program that we were in: GFSA: AI First. Part of the team was also in Singapore for Hatch’s DX3 onboarding. Intel Ignite just released a podcast featuring Nayul as a guest on one of its interview series: She Leads Deep Tech. And many more…
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The Demo Day & The Podcast
?We are nowhere near the point of diminishing marginal returns on how powerful we can make AI models as we increase the scale of compute.? - Kevin Scott, Microsoft Build 2024
AI Trends
Plan Early and Stay Informed: Knowing Your Infrastructural Needs for Successfully and Sustainably Productionizing Your AI-Based Solution
A couple of weeks ago, McKinsey released a report titled: The state of AI in early 2024: Gen AI adoption spikes and starts to generate value.
As the report title suggests, in a number of segments across a range of industries, we’re seeing many more cases of AI adoption translating into material business gains. With this, we’re also seeing greater optimism and shift among enterprises yet to adopt AI toward leveraging it for their own business goals.
In the AI operation side of things, it’s worth noting that the baseline cost of owning AI at the enterprise level tends to be high, with most expenses typically coming from hardware purchases (including access to high-end GPU compute time) and electricity use. Enterprises looking to adopt AI for these benefits can expect to shell out a hefty sum upfront—and even more for gen AI given its compute- and resource-intensive nature.
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Included in this sum are costs from running inference, which—unlike the one-time cost of model training—are recurrent and can very well become enterprises’ largest source of expenditure.
All of these costs can pose a barrier to AI adoption.
Deep-pocketed companies with a lot of resources to spare are no exceptions to the challenges that gen AI brings. The sheer amount of compute resources that gen AI models require often comes with performance limitations like latency issues, making these models ill-suited for real-time and real-life applications without a robust supporting infrastructure (we will go over this in more detail in our blog).
It won’t be long before enterprises start looking into infrastructural improvements like tools for optimizing inference, compressing models, and cheaper and/or more efficient hardware to mitigate these problems. Early consideration of these infrastructural needs during planning is important for AI adopters.
On another note, both the current and prospective energy consumption of gen AI is becoming alarming, as expressed in the report as an environmental concern. Enterprises of all sizes using/building/serving AI have a responsibility and duty to keep their energy use and emissions under control at sustainable levels; this should especially be the case for enterprises with a larger global and environmental footprint. AI is a great technology, but with it also comes great responsibility.
Start integrating CLIKA’s solution into your planning process to ensure a sustainable and efficient adoption of AI. We are an auto lightweight AI toolkit provider, where optimizing inference for edge devices and diverse hardware is at the core of our tech and service. With us, you get:
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We have a lot to offer; learn about them all: ?? Contact Us
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