Cerebras Slays GPUs, Breaks Record for Largest AI Models Trained on a Single Device

Cerebras Slays GPUs, Breaks Document for Largest AI Fashions Skilled on a Single Gadget

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Cerebras, the corporate behind the world’s largest accelerator chip in existence, the CS-2 Wafer Scale Engine, has simply introduced a milestone: the coaching of the world’s largest NLP (Pure Language Processing) AI mannequin in a single gadget. Whereas that in itself may imply many issues (it would not be a lot of a file to interrupt if the earlier largest mannequin was skilled in a smartwatch, as an illustration), the AI mannequin skilled by Cerebras ascended in the direction of a staggering – and unprecedented – 20 billion parameters. All with out the workload having to be scaled throughout a number of accelerators. That is sufficient to suit the web’s newest sensation, the image-from-text-generator, OpenAI’s 12-billion parameter DALL-E (opens in new tab).

A very powerful bit in Cerebras’ achievement is the discount in infrastructure and software program complexity necessities. Granted, a single CS-2 system is akin to a supercomputer all by itself. The Wafer Scale Engine-2 – which, just like the title implies, is etched in a single, 7 nm wafer, often sufficient for a whole lot of mainstream chips – contains a staggering 2.6 trillion 7 nm transistors, 850,000 cores, and 40 GB of built-in cache in a bundle consuming round 15kW.

Cerebras’ Wafer Scale Engine-2 in all its wafer-sized glory. (Picture credit score: Cerebras)

Maintaining as much as 20 billion-parameter NLP fashions in a single chip considerably reduces the overhead in coaching prices throughout 1000’s of GPUs (and related {hardware} and scaling necessities) whereas eliminating the technical difficulties of partitioning fashions throughout them. Cerebras says that is “some of the painful features of NLP workloads,” typically “taking months to finish.”

It is a bespoke drawback that is distinctive not solely to every neural community being processed, the specs of every GPU, and the community that ties all of it collectively – parts that have to be labored out upfront earlier than the primary coaching is ever began. And it might’t be ported throughout programs.

Cerebras CS-2

Cerebras’ CS-2 is a self-contained supercomputing cluster that features not solely the Wafer Scale Engine-2, but additionally all related energy, reminiscence and storage subsystems. (Picture credit score: Cerebras)

Pure numbers could make Cerebras’ achievement look underwhelming – OpenAI’s GPT-3, an NLP mannequin that may write total articles that could typically idiot human readers, contains a staggering 175 billion parameters. DeepMind’s Gopher, launched late final 12 months, raises that quantity to 280 billion. The brains at Google Mind have even introduced the coaching of a trillion-parameter-plus mannequin, the Change Transformer.

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