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Comment by r00fus | Hacker News Reader

I don’t think even enthusiasts can fully engage a 64 core device with any meaningful uses that a 32 core wouldn’t suffice.

This is a server chip that excels at virtualization.

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Then you are not a target. There are many scientific workloads that actually can benefit from shared memory multi core processors like this. They do not scale properly in cluster. Data Science is very simple and obvious target. Developers also can benefit a lot. Have you worked on a very large project of mixed c++ and Fortran + for some reason python (Compiling or profiling)? This is god send. I am currently working on a single node with 2x28 core and I still need more cores. For licensing issues I cannot use more than one node for my task.

I think most of the people finding themselves here would be in the “I wait on my computer several times a day” category. Data science and local compilation will see benefits for any amount of core scaling. One could argue all of this could be done on a server using a remote terminal, but that isn’t how many workflows are set up.

Try compiling Chromium or LLVM and tell me you don't want more than 32 cores.

Also there are plenty of embarrassingly parallel operations like 3D rendering, video editing, password cracking, and so on that would benefits. Ok usually you can use GPUs for that sort of thing but not always.

Tell that to someone who is running 112 threads at 100% doing Reinforcement Learning... Overall, Machine Learning enthusiast would eat any core they could get. With 64 cores Threadripper might even get close to 5TFlops and act as an older GPU for Deep Learning training.

I could definitely fully engage a 64 core CPU for fuzzing.

What you say is logical, but... have you opened your web browser's task manager recently?

3D rendering and video encoding need every bit of power they can get.

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