British chip designer Graphcore recently unveiled the Colossus MK2, also known as the GC200 IPU (Intelligence Processing Unit), which it calls the world’s most complex chip for AI applications. The chip offers eight times the performance of its predecessor, the Colossus MK1, and is powered by 59.4 billion transistors — which surpasses the 54 billion transistors in NVIDIA’s (NASDAQ:NVDA) newest top-tier A100 data center GPU.
Graphcore plans to install four GC200 IPUs into a new machine called the M2000, which is roughly the size of a pizza box and delivers one petaflop of computing power. On its own, the system is slower than NVIDIA’s A100, which can handle five petaflops on its own.
But Graphcore’s M2000 is a plug-and-play system that allows users to link up to 64,000 IPUs together for 16 exaflops (each exaflop equals 1,000 petaflops) of processing power. To put that into perspective, a human would need to perform a single calculation every second for nearly 31.7 billion years to match what a one exaflop system can do in a single second.
The GC200 and A100 are both clearly very powerful machines, but Graphcore enjoys three distinct advantages against NVIDIA in the growing AI market.
Image source: Getty Images.
1. Graphcore is developing custom chips for AI tasks
Unlike NVIDIA, which expanded its GPUs beyond gaming and professional visualization purposes into the AI market, Graphcore designs custom IPUs, which differ from GPUs or CPUs, for machine learning tasks.
Image source: Getty Images.
On its website, Graphcore claims: “CPUs were designed for office apps, GPUs for graphics, and IPUs for machine intelligence.” It explains that CPUs are designed for “scalar” processing, which processes one piece of data at a time, and GPUs are designed for “vector” processing, which processes a large array of integers and floating-point numbers at once.
Graphcore’s IPU technology uses “graph” processing, which processes all the data mapped across a single graph at once. It claims the IPU structure processes machine-learning tasks more efficiently than CPUs and GPUs. Many machine-learning frameworks — including TensorFlow, MXNet, and…
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