I gave Qwen 3.8 27B a reverse-engineering job I assumed needed a frontier model, and it finished in 30 minutes

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?

Yeah so I’ve been working with Claude on that. Typically it greps to find what it’s looking for and that can be a lot of tokens. So I created something halfway between rag and that (semantic search), and overall it lowers token usage a bit, but even if it optimistically reduces tokens 20%, Claude is hungry for docs.

There is automatic compaction, but I typically want to control that myself when I change focus (if I don’t just /clear it). Still I’d say almost all of the stuff I do runs north of 100k tokens.

Surprised the community hasn’t found better ways to reduce context, 100k is insane. Is it possible to make the agent work on smaller pieces at a time, so less context is needed per piece?