Intel Cannon Lake: 10nm chips finally make their way into the wild

They’ve been a long time coming, but Intel is finally shipping its first Cannon Lake CPUs, albeit in very limited quantities. 

According to Ars Technica, the first recipient of the next generation of chip is the Lenovo IdeaPad 330, a machine that only appears to be available via Chinese retailers at present.

Further details come via Intel’s own site, which details a 2-core/4-thread CPU clocked at 2.2GHz base, with 4MB of level 3 cache.

However, despite the specs of this chip now having been quietly made public, Cannon Lake is still far from being officially released. For one thing it’s still technically labelled an 8th-generation chip, which is the same as Intel’s previous 14nm Kaby Lake and Coffee Lake processors, and the CPU appears to be an OEM exclusive rather than being widely available to systems builders.

All this means that while we’re technically now in the 10nm era, it’s still far from being mainstream.

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Moore’s law creaks to a halt

Intel originally had much more ambitious plans for the first generation of 10nm CPUs, and had at one point intended to release them way back in 2015. However, over time the difficulties of shrinking its chips became more and more apparent, eventually forcing Intel to abandon its famous ‘tick-tock’ model to make way for a much more sedate tick-tock-tock approach.

This means that although it took just two years to jump from 22nm to 14nm, we’ve already been on 14nm for over three years now. Intel promised that we’d see Cannon Lake before the end of the year back at CES 2017, but there’s still no widespread release on the table despite Intel proudly proclaiming that Moore’s law wasn’t dead back in October last year.

But despite the slowdown in chip progress, there’s some speculation that network speeds and cloud computing are picking up some of the slack. With 4G internet now being widespread, and 5G on the horizon, devices are able to offload more and more intensive computing operations to the cloud rather than processing them on-device.

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