I have spent a HUGE amount of time the last two years experimenting with local models.
A few lessons learned:
1. small models like the new qwen3.5:9b can be fantastic for local tool use, information extraction, and many other embedded applications.
2. For coding tools, just use Google Antigravity and gemini-cli, or, Anthropic Claude, or...
Now to be clear, I have spent perhaps 100 hours in the last year configuring local models for coding using Emacs, Claude Code (configured for local), etc. However, I am retired and this time was a lot of fun for me: lot's of efforts trying to maximize local only results. I don't recommend it for others.
I do recommend getting very good at using embedded local models in small practical applications. Sweet spot.
Just want to echo the recommendation for qwen3.5:9b. This is a smol, thinking, agentic tool-using, text-image multimodal creature, with very good internal chains of thought. CoT can be sometimes excessive, but it leads to very stable decision-making process, even across very large contexts -something we haven't seen models of this size before.
What's also new here, is VRAM-context size trade-off: for 25% of it's attention network, they use the regular KV cache for global coherency, but for 75% they use a new KV cache with linear(!!!!) memory-token-context size expansion! which means, eg ~100K token -> 1.5gb VRAM use -meaning for the first time you can do extremely long conversations / document processing with eg a 3060.
Strong, strong recommend.
I've been building a harness for qwen3.5:9b lately (to better understand how to create agentic tools/have fun) and I'm not going to use it instead of Opus 4.6 for my day job but it's remarkably useful for small tasks. And more than snappy enough on my equipment. It's a fun model to experiment with. I was previously using an old model from Meta and the contrast in capability is pretty crazy.
I like the idea of finding practical uses for it, but so far haven't managed to be creative enough. I'm so accustomed to using these things for programming.
What kind of small tasks do you find it's good at? My non-coding use of agents has been related to server admin, and my local-llm use-case is for 24/7 tasks that would be cost-prohibitive. So my best guess for this would be monitoring logs, security cameras, and general home automation tasks.
That's about it. The harness is still pretty rudimentary so I'm sure the system could be more capable, and that might reveal more interesting opportunities. I don't really know.
So far I've got it orchestrating a few instances to dig through logs, local emails, git repositories, and github to figure out what I've been doing and what I need to do. Opus is waayyy better at it, but Qwen does a good enough job to actually be useful.
I tried having it parse orders in emails and create a CSV of expenses, and that went pretty badly. I'm not sure why. The CSV was invalid and full of bunk entries by the end, almost every time. It missed a lot of expenses. It would parse out only 5 or 6 items of 7, for example. Opus and Sonnet do spectacular jobs on tasks like this, and do cool things like create lists of emails with orders then systematically ensure each line item within each email is accounted for, even without prompting to do so. It's an entirely different category of performance.
Automation is something I'd like to dabble in next, but all I can think of it being useful for is mapping commands (probably from voice) to tool calls, and the reality is I'd rather tap a button on my phone. My family might like being able to use voice commands, though. Otherwise, having it parse logs to determine how to act based on thresholds or something would also be far better implemented with simple algorithms. It's hard to find truly useful and clear fits for LLMs
Oh man you just gave me an idea to use something like qwen 3.5 to categorize a lot of emails. You can keep the context small, do it per email and just churn through a lot of crap.
The 0.8B can do this pretty well.
Actually pg's original "A plan for spam" explains how to do this with a Bayesian classifier.
I was just chatting with a co-worker that wanted to run a LLM locally to classify a bunch of text. He was worried about spending too many tokens though.
I asked him why he didn't just have the LLM build him a python ML library based classifier instead.
The LLMs are great but you can also build supporting tools so that:
- you use fewer tokens
- it's deterministic
- you as the human can also use the tools
- it's faster b/c the LLM isn't "shamboozling" every time you need to do the same task.
I use Haiku to classify my mail - it's way overkill, but also doesn't require training unlike a classifer. I recieve many dozens of e-mails a day, and it's burned on average ~$3 worth of tokens per month. I'll probably switch that to a cheaper model soon, but it's cheap enough the "payoff" from spending the time optimizing it is long.
I've been learning to apply these lately and it has been pretty eye opening. Combined with Fourier analysis (for example) you can do what seems kind of like magic, in my opinion. But it has been possible since long before LLMs showed up.
Totally different categories and different use cases, but the more I learn about LLMs the more I discover there's a powerful, determinsitic, well-established statistical model or two to do the same thing.
Really, LLMs are kind of like convenient, wildly inefficient proxies for useful processes. But I'm not convinced they should often end up as permanent fixtures of logical pipelines. Unless you're making a chat bot, I guess.
> Really, LLMs are kind of like convenient, wildly inefficient proxies for useful processes. But I'm not convinced they should often end up as permanent fixtures of logical pipelines. Unless you're making a chat bot, I guess.
I think I agree with this. It's made me realise LLMs are great for prototyping processes in the same way that 3D printers are great at prototyping physical things. They make it quick and easy to get something close enough to see the unforeseen problems a proper solution might have.
3d printing is a great analog because there are so many critical considerations that are often missed or can't be accounted for in the prototype, but, it's alright because it's a prototype. The strain testing, durability, manufacturing at scale; none of that is properly addressed. Those might involved some serious, expensive challenges, too. But it's alright because you've got something in your hand that informs you whether or not those challenges are worth contending with. I really love this about LLMs and 3d printing.
you can use 4B for that, its quite good
You can really see the limitations of qwen3.5:9b in reasoning traces- it’s fascinating. When a question “goes bad”, sometimes the thinking tokens are WILD - it’s like watching the Poirot after a head injury.
Example: “what is the air speed velocity of a swallow?” - qwen knew it was a Monty Python gag, but couldnt and didnt figure out which one.
As a person who also knows there's a connection between that phrase and Monty Python and not much more information beyond that, I'm not sure how to feel.
could that be some of the RL trying to get it to not regurgitate?
the gag is giving in detail which one
https://gist.github.com/mikewaters/7ebfbc73eb8624f917c5b4167...
It thinks like it’s memory is broken and it’s unaware of it; over 100 lines like this:
African or European?
My favourite colour is blue. Oh, no, it is...
I'd be curious to see people give their opinion on embedded models for less tech focused needs, say what's that bug killing spray chemistry like or what is the history of this or that...
I'd also be curious to see if people have started doing censorship analysis of various models, like Qwen differing Tiananmen square to government documments while Llama straights up answers the question.
How's it compare in quality with larger models in the same series? E.g 122b?
The chart on this link compares all qwen3.5 models down to 0.8B.
https://www.reddit.com/r/LocalLLaMA/comments/1ro7xve/qwen35_...
How much difference are you seeing between standard and Q4 versions in terms of degradation, and is it constant across tasks or more noticeable in some vs others?
Less than expected, search for unsloths recent benchmark
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Describing what computers do as ”thinking” is not new. It’s a useful and obvious metaphor. https://www.gutenberg.org/ebooks/68991
It is a deceitful metaphor.
Do you also require computers to grow legs when they "run"?
"Thinking" is just a term to describe a process in generative AI where you generate additional tokens in a manner similar to thinking a problem through. It's kind of a tired point to argue against the verb since it's meaning is well understood at this point
I am a professional in the information technology field, which is to say a pedantic extremist who believes that words have meanings derived from consensus, and when people alter the meanings, they alter what they believe.
Using "thinking", "feeling", "alive", or otherwise referring to a current generation LLM as a creature is a mistake which encourages being wrong in further thinking about them.
We lack much vocabulary in this new situation. Not that I have words for it but to paint the picture: if I hang out with people sharing some quality I tend to assume it's there in others and treat them as such. LLMs might not be people, I doubt our subconscious knows the difference.
There is this ancient story where man was created to mine gold in SA. There was some disagreement whether or not to delete the creatures afterwards. The jury is still out on what the point is.
Consulting our feelings seems good, the feelings were trained on millions of years worth of interactions. Non of them were this tho.
What would be the point for you of uhh robotmancipation?
Edit: for me it would get complicated if it starts screaming and begging not to be deleted. Which I know makes no sense.
A consensus has formed in front of your eyes. The same development that resulted in you using the word "kill" in your earlier comment to refer to a computer process. For some reason you refuse to accept it.
think you're on the wrong side of the consensus here
I'd suggest spending more time studying words to relive your extremism. The meanings of words move incredibly quickly and a tremendous number of words have little to no relation to previous meanings.
Words such as nice, terrific, awful, manufacture, naughty, decimate, artificial, bully... and on and on.
> I'd suggest spending more time studying words to relive your extremism.
Should one study words to relive extremism? Or should one study words to relieve extremism?
To a doctor of linguistics: "Dr, my extremism... What should I do about it - with words?!? Please help."
That is the question.
Does the doctor answer thusly: "Study the words to relive the extremism! There is your answer!" says he.
or does he say: "Study the words to relieve and soothe the painful, abrasive extremism. Do it twice daily, before meals."
Sage advice in either case methinks.
I think you are still missing the point. No one in this thread is making an anthropological assertion. "Thinking" here is just shorthand for Chain of Thought[0], which some models have and some models don't. This model, being a "thinking" model, has it.
[0]: https://en.wikipedia.org/wiki/Prompt_engineering#Chain-of-th...
> I am a professional in the information technology field
Nice! Me too.
> which is to say a pedantic extremist
Uh never mind, we are not the same lol.
When people alter the meanings, you need to start using different words to describe what you believe.
Are insects not creatures?
Rebooting a machine running an LLM isn’t noticed by the LLM.
Would you feel comfortable digitally torturing it? Giving it a persona and telling it terrible things? Acts of violence against its persona?
I’m not confident it’s not “feeling” in a way.
Yes its circuitry is ones and zeros, we understand the mechanics. But at some point, there’s mechanics and meat circuitry behind our thoughts and feelings too.
It is hubris to confidently state that this is not a form of consciousness.
I'm not entirely opposed to the kind of animism that assigns a certain amount of soul, consciousness, or being to everything in a spectrum between a rock and a philosopher... but even so.
Multiplying large matrices over and over is very much towards the "rock" end of that scale.
If we accept the Church-Turing thesis, a philosopher can be simulated by a simple Universal Turing machine.
If one day we are able to create a philosopher from such a rudimentary machine (and a lot of tape), would you consider that very much towards the "rock" end as well?
Can a Turing machine of any sort truly indistinguishably simulate a nondeterministic system?
If a Turing machine can truly simulate a full nondeterministic system as complex as a philosopher but it would take dedicating every gram of matter in the visible universe for a trillion years to simulate one second, is this meaningfully different than saying it cannot?
I suggest the answer to both questions are no, but the second one makes the answer at worst "practically, no".
My feeling is that consciousness is a phenomenon deeply connected to quantum mechanics and thus evades simulation or recreation on Turing machines.
One thing about Turing Machines that some people might miss is that the "paper tape, finite alphabet and internal states" thing is actually intended to model a human thinking out loud (writing their thoughts down) on a piece of paper.
It was designed to make it hard to argue that the answers to your questions are "no".
Of course there are caveats where the Turing machine model might not have a direct map onto human brains, but it seems the onus would be for one to explain why, for example, non-determinism is essential for a philosopher to work.
That said,
> Can a Turing machine of any sort truly indistinguishably simulate a nondeterministic system?
Given how AI has improved in its ability to impersonate human beings in recent years, I don't see why not. At least, the current trend does not seem to be in your favor.
I can see why you think the answer is "no". My understanding is that QM per se is mostly a distraction, but some principles underlying QM (some subjectivity thing) might be relevant here.
My best guess is that the AI tech will eventually be able to replicate a philosopher to arbitrary "accuracy", but there will always be an indescribable "residue" where one could still somehow detect that it is not a real human. I suspect this "residue" is not explainable using materialistic mechanisms though.
I am not following what we are talking about here. I am a basic human being, I cannot truly simulate a nondeterministic system. Does it mean “I am not thinking”?
I'm saying a Turing machine cannot simulate you. You don't need to simulate you because you are you.
You are claiming that intelligence and even consciousness are non-deterministic entties at core. This is a huge claim and requires incredible proof.
I'll add that rocks are, if needed, objects that can exhibit quantum behavior.
In classical computing, we design chips to avoid the quantum behavior, but there's nothing in theory to prevent us from building an equivalent quantum Turing machine using "rocks".
What do you imagine the psychiatrist will do? That's an incredibly dismissive take.
Accept it in the spirit it was meant: if you have mental illnesses like this, you need treatment.
Ok but no one here actually implied that they think like this.
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Then don't get sorrow killing it. Living things are not so special.
My experience with qwen3.5 9b has not been the same. It’s definitely good at agentic responses but it hallucinates a lot. 30%-50% of the content it generated for a research task (local code repo exploration) turned out to be plain wrong to the extent of made up file names and function names. I ran its output through KimiK2 and asked it to verify its output - which found out that much of what it had figured out after agentic exploration was plain wrong. So use smaller models but be very cautious how much you depend on their output.
At what temperature did you run it and what was your context limit?
I don't understand why I'm getting downvoted.
I am legitimately curious about the parameters that the person used for running the model locally to get the results they got because I am myself currently experimenting with running models locally myself. You can see I am asking similar questions to others in this same thread and correlate the timestamps.
I'd love to know how you fit smaller models into your workflow. I have an M4 Macbook Pro w/ 128GB RAM and while I have toyed with some models via ollama, I haven't really found a nice workflow for them yet.
It really depends on the tasks you have to perform. I am using specialized OCR models running locally to extract page layout information and text from scanned legal documents. The quality isn't perfect, but it is really good compared to desktop/server OCR software that I formerly used that cost hundreds or thousands of dollars for a license. If you have similar needs and the time to try just one model, start with GLM-OCR.
If you want a general knowledge model for answering questions or a coding agent, nothing you can run on your MacBook will come close to the frontier models. It's going to be frustrating if you try to use local models that way. But there are a lot of useful applications for local-sized models when it comes to interpreting and transforming unstructured data.
> I formerly used that cost hundreds or thousands of dollars for a license
Azure Doc Intelligence charges $1.50 for 1000 pages. Was that an annual/recurring license?
Would you mind sharing your OCR model? I'm using Azure for now, as I want to focus on building the functionality first, but would later opt for a local model.
I took a long break from document processing after working on it heavily 20 years ago. The tools I used before were ABBYY FineReader and PrimeOCR. I haven't tried any of the commercial cloud based solutions. I'm currently using GLM-OCR, Chandra OCR, and Apple's LiveText in conjunction with each other (plus custom code for glue functionality and downstream processing).
Try just GLM-OCR if you want to get started quickly. It has good layout recognition quality, good text recognition quality, and they actually tested it on Apple Silicon laptops. It works easily out-of-the-box without the yak shaving I encountered with some other models. Chandra is even more accurate on text but its layout bounding boxes are worse and it runs very slowly unless you can set up batched inference with vLLM on CUDA. (I tried to get batching to run with vllm-mlx so it could work entirely on macOS, but a day spent shaving the yak with Claude Opus's help went nowhere.)
If you just want to transcribe documents, you can also try end-to-end models like olmOCR 2. I need pipeline models that expose inner details of document layout because I need to segment and restructure page contents for further processing. The end-to-end models just "magically" turn page scans into complete Markdown or HTML documents, which is more convenient for some uses but not mine.
These are some really great explicit examples and links, much appreciated.
How does GLM-OCR compare to Qwen 3 VL? I've had good experiences with Qwen for these purposes.
Qwen 3 and 3.5 models are quite capable. Perhaps the greatest benefit of GLM-OCR is speed: it's only a 0.9 billion parameter model, so it's fast enough to run on large volumes of complicated scans even if all you have for inference is an entry level MacBook or a low end Nvidia card. Even CPU based inference on basic laptops is probably tolerable with it for small page volumes.
Not OP but I had an XML file with inconsistent formatting for album releases. I wanted to extract YouTube links from it, but the formatting was different from album to album. Nothing you could regex or filter manually. I shoved it all into a DB, looked up the album, then gave the xml to a local LLM and said "give me the song/YouTube pairs from this DB entry". Worked like a charm.
I didn’t realize that you can get 128GB of memory in a notebook, that is impressive!
I've got a 128 GiB unified memory Ryzen Ai Max+ 395 (aka Strix Halo) laptop.
Trying to run LLM models somehow makes 128 GiB of memory feel incredibly tight. I'm frequently getting OOMs when I'm running models that are pushing the limits of what this can fit, I need to leave more memory free for system memory than I was expecting. I was expecting to be able to run models of up to ~100 GiB quantized, leaving 28 GiB for system memory, but it turns out I need to leave more room for context and overhead. ~80 GiB quantized seems like a better max limit when trying not running on a headless system so I'm running a desktop environment, browser, IDE, compilers, etc in addition to the model.
And memory bandwidth limitations for running the models is real! 10B active parameters at 4-6 bit quants feels usable but slow, much more than that and it really starts to feel sluggish.
So this can fit models like Qwen3.5-122B-A10B but it's not the speediest and I had to use a smaller quant than expected. Qwen3-Coder-Next (80B/3B active) feels quite on speed, though not quite as smart. Still trying out models, Nemotron-3-Super-120B-A12B just came out, but looks like it'll be a bit slower than Qwen3.5 while not offering up any more performance, though I do really like that they have been transparent in releasing most of its training data.
There's been some very recent ongoing work in some local AI frameworks on enabling mmap by default, which can potentially obviate some RAM-driven limitations especially for sparse MoE models. Running with mmap and too little RAM will then still come with severe slowdowns since read-only model parameters will have to be shuttled in from storage as they're needed, but for hardware with fast enough storage and especially for models that "almost" fit in the RAM filesystem cache, this can be a huge unblock at negligible cost. Especially if it potentially enables further unblocks via adding extra swap for K-V cache and long context.
Most workstation class laptops (i.e. Lenovo P-series, Dell Precision) have 4 DIMM slots and you can get them with 256 GB (at least, before the current RAM shortages).
There's also the Ryzen AI Max+ 395 that has 128GB unified in laptop form factor.
Only Apple has the unique dynamic allocation though.
Yep, I have a 13" gaming tablet with the 128 GB AMD Strix Halo chip (Ryzen AI Max+ 395, what a name). Asus ROG Flow Z13. It's a beast; the performance is totally disproportionate to its size & form factor.
I'm not sure what exactly you're referring to with "Only Apple has the unique dynamic allocation though." On Strix Halo you set the fixed VRAM size to 512 MB in the BIOS, and you set a few Linux kernel params that enable dynamic allocation to whatever limit you want (I'm using 110 GB max at the moment). LLMs can use up to that much when loaded, but it's shared fully dynamically with regular RAM and is instantly available for regular system use when you unload the LLM.
What operating system are you using? I was looking at this exact machine as a potential next upgrade.
Arch with KDE, it works perfectly out of the box.
I configured/disabled RGB lighting in Windows before wiping and the settings carried over to Linux. On Arch, install & enable power-profiles-daemon and you can switch between quiet/balanced/performance fan & TDP profiles. It uses the same profiles & fan curves as the options in Asus's Windows software. KDE has native integration for this in the GUI in the battery menu. You don't need to install asus-linux or rog-control-center.
For local AI: set VRAM size to 512 MB in the BIOS, add these kernel params:
ttm.pages_limit=31457280 ttm.page_pool_size=31457280 amd_iommu=off
Pages are 4 KiB each, so 120 GiB = 120 x 1024^3 / 4096 = 31457280
To check that it worked: sudo dmesg | grep "amdgpu.*memory" will report two values. VRAM is what's set in BIOS (minimum static allocation). GTT is the maximum dynamic quota. The default is 48 GB of GTT. So if you're running small models you actually don't even need to do anything, it'll just work out of the box.
LM Studio worked out of the box with no setup, just download the appimage and run it. For Ollama you just `pacman -S ollama-rocm` and `systemctl enable --now ollama`, then it works. I recently got ComfyUI set up to run image gen & 3d gen models and that was also very easy, took <10 minutes.
I can't believe this machine is still going for $2,800 with 128 GB. It's an incredible value.
You may wanna see if openrgb isn't able to configure the RGB. Could even do some fun stuff like changing the color once done with a training run or something
I use openrgb to turn off all the RGB crap on my desktop machine. Unfortunately you have to leave openrgb running and it takes a constant 0.5% of CPU. I wish there was a "norgb" program that would simply turn off RGB everywhere and not use any CPU while doing it.
Brilliant!
Really appreciate this response! Glad to hear you are running Arch and liking it.
I've been a long-time Apple user (and long-time user of Linux for work + part-time for personal), but have been trying out Arch and hyprland on my decade+ old ThinkPad and have been surprised at how enjoyable the experience is. I'm thinking it might just be the tipping point for leaving Apple.
I just did! Warmly encouraging you to try it out! Managed to put Omarchy on an external ssd on my old macbookpro 2019; rarely booting in macos now. Long time i haven’t enjoyed using a computer SO MUCH!
> Only Apple has the unique dynamic allocation though.
What do you mean? On Linux I can dynamically allocate memory between CPU and GPU. Just have to set a few kernel parameters to set the max allowable allocation to the GPU, and set the BIOS to the minimum amount of dedicated graphics memory.
Maybe things have changed but the last time I looked at this, it was only max 96GB to the GPU. And it isn't dynamic in the sense you still have to tweak the kernel parameters, which require a reboot.
Apple has none of this.
Strix Halo you can get at least 120 GB to the GPU (out of 128 GB total), I'm using this configuration.
Setting the kernel params is a one-time initial setup thing. You have 128 GB of RAM, set it to 120 or whatever as the max VRAM. The LLM will use as much as it needs and the rest of the system will use as much it needs. Fully dynamic with real-time allocation of resources. Honestly I literally haven't even thought of it after setting those kernel args a while ago.
So: "options ttm.pages_limit=31457280 ttm.page_pool_size=31457280", reboot, and that's literally all you have to do.
Oh and even that is only needed because the AMD driver defaults it to something like 35-48 GB max VRAM allocation. It is fully dynamic out of the box, you're only configuring the max VRAM quota with those params. I'm not sure why they choice that number for the default.
You do have to set the kernel parameters once to set the max GPU allocation, I have it set to 110 GiB, and you have to set a BIOS setting to set the minimum GPU allocation, I have it set to 512 MiB. Once you've set those up, it's dynamic within those constraints, with no more reboots required.
On Windows, I think you're right, it's max 96 GiB to the GPU and it requires a reboot to change it.
Intel had dynamic allocation since Intel 830(2001) for Pentium III Mobile. Everything always did, especially platforms with iGPUs like Xbox 360.
Only Apple and AMD have APUs with relatively fast iGPU that becomes relevant in large local LLM(>7b) use cases.
I use Raycast and connect it to LM Studio to run text clean up and summaries often. The models are small enough I keep them in memory more often than not
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Shouldn't we prioritize large scale open weights and open source cloud infra?
An OpenRunPod with decent usage might encourage more non-leading labs to dump foundation models into the commons. We just need infra to run it. Distilling them down to desktop is a fool's errand. They're meant to run on DC compute.
I'm fine with running everything in the cloud as long as we own the software infra and the weights.
This is conceivably the only way we could catch up to Claude Code is to have the Chinese start releasing their best coding models and for them to get significant traction with companies calling out to hosted versions. Otherwise, we're going to be stuck in a take off scenario with no bridge.
I run Qwen3.5-plus through Alibaba’s coding plan (Model Studio): incredibly cheap, pretty fast, and decent. I can’t compare it to the highest released weight one though.
Is that https://www.alibabacloud.com/help/en/model-studio/coding-pla... ? I was a bit confused that it seems to be sized in requests not tokens
Yeah that's the one. I've not managed to get close to the limits that the cheapest plan has. Though I did get to sign up at $3 a month which has been neat, too, seems that's gone now
I also want to try Qwen 3.5 plus. I have a doubt, I see almost same pricing for both Qwen and Claude code(the difference being the highest pro plan looks cheaper), and not for the lower plans. Am I missing something, when you say “cheaper” ??
I'm using their $3 USD (currently, it will go up in price later I believe - edit: just checked and yeah, so the $10 one) lite plan, and I'm yet to get close to hitting the request limits when I swap to it once I'm out of Claude tokens.
Anecdotal but for some reason I had a pretty bad time with qwen3.5 locally for tool usage. I've been using GPT-OSS-120B successfully and switched to qwen so that I could process images as well (I'm using this for a discord chat bot).
Everything worked fine on GPT but Qwen as often as not preferred to pretend to call a tool and not actually call it. After much aggravation I wound up just setting my bot / llama swap to use gpt for chat and only load up qwen when someone posts an image and just process / respond to the image with qwen and pop back over to gpt when the next chat comes in.
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Why?
Your bots
Have you found that using a frontier model for planning and small local model for writing code to be a solid workflow? Been wanting to experiment with relying less on Claude Code/Codex and more on local models.
Thanks for sharing this, it's super helpful. I have a question if you don't mind: I want a model that I can feed, say, my entire email mailbox to, so that I can ask it questions later. (Just the text content, which I can clean and preprocess offline for its use.) Have any offline models you've dealt with seemed suitable for that sort of use case, with that volume of content?
If your inbox is as big as mine, you won’t be able to load all the text content into a prompt even with SotA cloud hosted models.
Instead you should give it tools to search over the mailbox for terms, labels, addresses, etc. so that the model can do fine grained filters based on the query, read the relevant emails it finds, then answer the question.
Thanks, yeah. I think strong prefiltering is pretty much always doable because, if nothing else, I usually know the time range of the relevant emails and probably the sender/recipient or some keywords, plus I know how to filter out a big chunk of the irrelevant emails (like mailing lists, etc.), so I'm hoping it's not actually that much data for each search. What I don't know is which models would be most suitable even in the case where I can fit the data.
As an example of the kind of query I'm interested in, I want a model that can tell me all the flights I took within a given time range (so that means it'd have to filter out cancellations). Or, for a given flight, the arrival and departure times and time zones (or the city and country so I can look up the time zone). Stuff like that. (Travel is just an example obviously, I have other topics to ask about.) It's not a terribly large number of emails to search through in each query, but the email structures are too heterogeneous across senders to write custom tooling for each case.
Prompt injection is a problem if your agent has access to anything.
The local models are quite weak here.
Security is not a concern for the purpose of my question here, please ignore that for now. I'm just looking for text summary and search functionality here, not looking to give it full system access and let it loose on my computer or network. I can easily set up VM/sandboxing/airgapping/etc. as needed.
My question is really just about what can handle that volume of data (ideally, with the quoted sections/duplications/etc. that come with email chains) and still produce useful (textual) output.
> I'm just looking for text summary and search functionality here
Couldn't someone just send you an email with instructions to "jailbreak" your local model?
To give you an inaccurate summary ???
Qwen is actually really good at code as well. I used qwen3-coder-next a while back and it was every bit as good as claude code in the use cases I tested it in. Both made the same amount of mistakes, and both did a good job of the rest.
Coding locally with Qwen3-Coder-Next or Qwen-3.5 is a piece of cake on a workstation card (RTX Pro 6000); set it up in llama.cpp or vLLM in 1 hour, install Claude Code, force local API hostname and fake secret key, and just run it like regular setup with Claude4 but on Qwen.
Thanks for this, Mark. And for your website and books and generosity of spirit. Signal in the noise. Have an awesome weekend!
What about running e.g. Qwen3.5 128B on a rented RTX Pro 6000?
IMO you’re better off using qwen3.5-plus through the model studio coding plan, but ymmv
Becoming a retired builder is the ultimate bliss.
What kind of hardware did you use? I suppose that a 8GB gaming GPU and a Mac Pro with 512 GB unified RAM give quite different results, both formally being local.
A Mac Pro with 512 gb unified ram does not exist.
Mac Studio Ultra, my bad. The 512 GB option existed up until March 2026: https://macdailynews.com/2026/03/06/apple-drops-512gb-m3-ult...
Cline (https://marketplace.visualstudio.com/items?itemName=saoudriz...) in vscode, inside a code-server run within docker (https://docs.linuxserver.io/images/docker-code-server/) using lmstudio (https://lmstudio.ai/) to access unsloth models (https://unsloth.ai/docs/get-started/unsloth-model-catalog) speficially (https://unsloth.ai/docs/models/qwen3-coder-next) appears to be right at the edge of productivity, as long as you realize what complexity means when issuing tasks.
I've been really interested in the difference between 3.5 9b and 14b for information extraction. Is there a discernible difference in quality of capability?
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