i have always said the ai bubble is data centers. thr fuyure is local. what surprised me was thst nvidia has already shifted. those condumer rtx spark pcs coming out soon look awesome.

I agree, but this will help pop the bubble because local models we can run on our own hardware will crash the demand for data centers

I was issued a laptop with 32GB for standard office work. 15 years ago our devs had 128GB. Of course, that was before COVID and AI insanity.

Gorgon Halo, IIRC, goes up to a unified 192GB.

And while it depends on application, I generally agree that amount of memory is the most important factor. I started out with a 24GB RX 7950 XTX and then picked up a 128GB Framework Desktop. The larger amount of memory on the Framework is just a lot more useful than the greater bandwidth on the 7950, gives a lot more flexibility. I was always able to find useful things to do with more memory and could use more. For LLMs, more context, larger models, less quantitization. For image diffusion models, larger models, higher native resolutions without tradeoffs like having an upscaling pass, batch passes.

You can see why the cloud AI companies are hell-bent on getting all the memory that they can get their paws on.

I'm surprised you feel you need that much. Personally, I'm tinkering on a 7900XTX, with 24Gb of VRAM. I can't run the massive new models, like the latest Deepseek Flash at 400+ Gb, but neither can 192Gb. Means my context isn't unlimited, but I'm able to run even big local models like Qwen3.8 at Q4_K_M with a context of 64K, which is enough when paired with a front end like Hermes that can run compaction and invoke subagents. Honestly, I'd describe 24Gb as a sweet spot for local AI usage, 32Gb sounds like comfortable headroom for a larger context window, or even multiple heads.

To be fair, I guess it is unified memory, and it doesn't sound like you're running it on a dedicated machine, so maybe the other stuff you're running is pushing past 8Gb and giving you less space for AI than me. But 128Gb still sounds like wild excess to me, well into the diminishing returns of slightly more accurate rounding and needlessly large context.

You can give the model access to a tool like ghidra via a MCP

Better yet, give your model access to a CLI and save on context window

This is the current challenge, MCP and standards are helping to define the protocols, but harnesses and setting guardrails are almost as important as a quality model.