Nice, but I want lower prices for the RAM and SSD modules.
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
Where do you think those models are trained?
And most personal computing has tended towards only having a smartphone, and a laptop for a real PC. You can’t run much in the way of an LLM on those (at least not with any real speed).
128 GB of unified memory
I thought I was asking a lot when work asked me to pick a computer (with no guidelines) and I asked for a 36GB MacBook and justified it by saying I needed to run local models to save money. I didn’t ask for nearly enough. To its credit it does run local models fast, but it’s very limited in context window. It starts slowing down long before hitting context sizes I hit in frontier models.
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.
64k context is nowhere near enough. I try to remember to compact Claude when I hit 200k but sometimes when I’m not paying attention I can hit 600k. Yeah I’ve written little Python scripts on my machine using qwen, but some of my work projects have 80k tokens of just documentation.
some of my work projects have 80k tokens of just documentation
I feel like there’s got to be better approaches than just shoving the entire documentation into the context. Humans don’t try to memorize the entire documentation. We just search for relevant pieces and remember those, or have them open in a webpage alongside the code. We probably only have like 10k context maximum.
Do the modern agent harnesses not have automatic compaction?
