hpc

2 Articles

Regretfully: $3,000 Worth Of Raspberry Pi Boards

September 20, 2025 by 53 Comments

We feel for [Jeff Geerling]. He spent a lot of effort building an AI cluster out of Raspberry PI boards and $3,000 later, he’s a bit regretful. As you can see in the video below, it is a neat build. As Jeff points out, it is relatively low power and dense. But dollar for dollar, it isn’t much of a supercomputer.

Of course, the most obvious thing is that there’s plenty of CPU, but no GPU. We can sympathize, too, with the fact that he had to strip it down twice and rebuild it for a total of three rebuilds. One time, he decided to homogenize the SSDs for each board. The second time was to affix the heatsinks. It is always something.

With ten “blades” — otherwise known as compute modules — the plucky little computer turned in about 325 gigaflops on tests. That sounds pretty good, but a Framework Desktop x4 manages 1,180 gigaflops. What’s more is that the Framework turned out cheaper per gigaflop, too. Each dollar bought about 110 megaflops for the Pis, but about 140 for the Framework.

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The 13.5 Million Core Computer

November 23, 2022 by 55 Comments

Having a dual- or quad-core CPU is not very exotic these days and CPUs with 12 or even 16 cores aren’t that rare. The Andromeda from Cerebras is a supercomputer with 13.5 million cores. The company claims it is one of the largest AI supercomputers ever built (but not the largest) and can perform 120 Petaflops of “dense compute.”

We aren’t sure about the methodology, but they also claim more than one exaflop of “AI computing.” The computer has a fabric backplane that can handle 96.8 terabits per second between nodes. According to a post on Extreme Tech, the core technology is a 3-plane wafer processor, WSE-2. One plane is for communications, one holds 40 GB of static RAM, and the math plane has 850,000 independent cores and 3.4 million floating point units.

The data is sent to the cores and collected by a bank of 64-core AMD EPYC 3 processors. Andromeda is optimized to handle sparse matrix computations. The company claims that the performance scales “almost linearly.” That is, as you double the number of cores used, you roughly half the total run time.

The machine is available for remote use and cost about $35 million to build. Since it uses 500 kW at peak run times, it isn’t free to operate, either. Extreme Tech notes that the Frontier computer at Oak Ridge National Labs is both larger and more precise, but it cost $600 million, so you’d expect it to be more capable.

Most homebrew “supercomputers” we see are more for learning how to work with clusters than trying to hit this sort of performance. Of course, if you have a modern graphics card, OpenCL and CUDA will let you do some of this, too, but at a much lesser scale.