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?
Oh for sure. Agents often use under 30k tokens, but the orchestrator needs to have enough information to instruct the agents so it typically is a fair bit bigger in context. Agents tend to save you money on frontier models, but I’m skeptical about local LLMs. I suppose if you aren’t pressed for time it’s probably just fine. I haven’t played that much with it because anything big enough to bother with agents I typically feel is too big for local anyway. But I’m sure others have experimented more on that front than I have.
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 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.
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?
Oh for sure. Agents often use under 30k tokens, but the orchestrator needs to have enough information to instruct the agents so it typically is a fair bit bigger in context. Agents tend to save you money on frontier models, but I’m skeptical about local LLMs. I suppose if you aren’t pressed for time it’s probably just fine. I haven’t played that much with it because anything big enough to bother with agents I typically feel is too big for local anyway. But I’m sure others have experimented more on that front than I have.
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.
What in the fuck bloated skylab hokey ass shit were your devs making 15 years ago that demanded that kind of rocketship?
Lunchtime video games.
But also SharePoint, virtual. Bloated crap.
Meanwhile I’ve got 16GB and opening a third webbrowser causes shit to randomly close… Lol
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.
Dis the same mistake. Feel you!