Electron dropped support for applications that require more than 4 GB of RAM (opens in new tab)

(github.com)

2 pointsMrResearcher1y ago3 comments

3 comments

3 comments · 1 top-level

MrResearcherOP1y ago· 2 in thread

Same problem with the modern-day Chrome. Gotta recompile it without memory caging just to let the web app to allocate more than 4GB of RAM.

You may argue that you don't want it. You might say that you don't want a damn web page to consume more than 4GB. And I agree, it's true for many applications. Up until the moment you decide to start dealing heavily with ML datasets, and all sort of high-resolution images, time series, bayesian inference etc. And now the limit is no longer sufficient.

We already had folks thinking that 64KB are surely enough for everyone. Then we introduced segment registers CS, DS, ES, SS to address more than 64KB. You remember that sad story? Then we got a breathing with 64-bit mode, with all pointers able to reference the entire memory (well, at least in ring 0.) Are we sure that 4GB are enough for everyone? Aren't we coming into the same trap again?

fragmede1y ago

I mean, it's a neat trick to download an ML model into my web browser's tab's RAM, and run inference against is using WebGPU, but this move is saying it's only that, a novelty that's limited to models smaller than 4 GiB. Because outside of that, the web browser is the frontend to the system, and while the backend server (which could be running locally) can take as much RAM as there is, they are, in fact, saying that 4 GiB should be enough for rendering what a person can see and interact with. Which is different than a limit imposed on all computing, assuming the client server model continues to be dominant. The limitation being imposed means that heavier lifting has to be done on a server somewhere instead of locally, so the "trap" is that computer manufacturers have maxed out computers with 16 GiB of RAM in 2024 (or even 8, hi Apple). Except that 16 has been the max on many systems since like, 2016.

So it's a measure to try and prevent laptops from having to grow memory requirements. It's like limiting pairs of shoes to only having two shoes. You can have many pairs of shoes, but you don't need three shoes at once.

MrResearcherOP1y ago

It's more akin to limiting the size of each shoe, rather than the number of shoes. The client might need memory for computational purposes. I routinely have more than 7 GB worth of inference output to be displayed. I can only display a portion of it at a time, and I react to every mouse move to render the ML annotations, which rely on that underlying 7GB array.

Laptops handle it just fine. I have no problems with macbook, for as long as there's no memory caging enforcement. I'd imaging, running it on a cell phone or a tablet should be feasible too.

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