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Can I run AI locally?

canirun.ai

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Re: Can I run AI locally?

#101

Post by J_Shelby_J »

mopierotti wrote: post #95

This (+ llmfit) are great attempts, but I've been generally frustrated by how it feels so hard to find any sort of guidance about what I would expect to be the most straightforward/common question: "What is the highest-quality model that I can run on my hardware, with tok/s greater than , and context limit greater than " (My personal approach has just devolved into guess-and-check, which is time consuming.) When usin…

It’s a hard problem. I’ve been working on it for the better part of a year.

Well, granted my project is trying to do this in a way that works across multiple devices and supports multiple models to find the best “quality” and the best allocation. And this puts an exponential over the project.

But “quality” is the hard part. In this case I’m just choosing the largest quants.

Re: Can I run AI locally?

#102

Post by [deleted] »

itigges22 wrote: post #88
tcbrah wrote: post #53

tbh i stopped caring about "can i run X locally" a while ago. for anything where quality matters (scripting, code, complex reasoning) the local models are just not there yet compared to API. where local shines is specific narrow tasks - TTS, embeddings, whisper for STT, stuff like that. trying to run a 70b model at 3 tok/s on your gaming GPU when you could just hit an API for like $0.002/req feels like a weird flex I…

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Re: Can I run AI locally?

#103

Post by kylehotchkiss »

mark_l_watson wrote: post #78

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 l…

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?

Re: Can I run AI locally?

#104

Post by radlad »

mark_l_watson wrote: post #78

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 l…

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.

Re: Can I run AI locally?

#105

Post by SXX »

Sorry if already been answered, but will there be a metric for latency aka time to first token?

Since I considered buying M3 Ultra and feel like it the most often discussed regarding using Apple hardware for runninh local LLMs. Where speed might be okay, but prompt processing can take ages.

Re: Can I run AI locally?

#108

Post by teaearlgraycold »

SXX wrote: post #105

Sorry if already been answered, but will there be a metric for latency aka time to first token? Since I considered buying M3 Ultra and feel like it the most often discussed regarding using Apple hardware for runninh local LLMs. Where speed might be okay, but prompt processing can take ages.

Wait for the M5 Ultra. It will get the 4x prompt processing speeds from the rest of the M5 product line. I hear rumors it will be released this year.

Re: Can I run AI locally?

#109

Post by lambda »

pbronez wrote: post #65
meatmanek wrote: post #40

This seems to be estimating based on memory bandwidth / size of model, which is a really good estimate for dense models, but MoE models like GPT-OSS-20b don't involve the entire model for every token, so they can produce more tokens/second on the same hardware. GPT-OSS-20B has 3.6B active parameters, so it should perform similarly to a 3-4B dense model, while requiring enough VRAM to fit the whole 20B model. (In term…

The docs page addresses this: > A Mixture of Experts model splits its parameters into groups called "experts." On each token, only a few experts are active — for example, Mixtral 8x7B has 46.7B total parameters but only activates ~12.9B per token. This means you get the quality of a larger model with the speed of a smaller one. The tradeoff: the full model still needs to fit in memory, even though only part of it run…

It discusses it, and they have data showing that they know the number of active parameters on an MoE model, but they don't seem to use that in their calculation. It gives me answers far lower than my real-world usage on my setup; its calculation lines up fairly well for if I were trying to run a dense model of that size. Or, if I increase my memory bandwidth in the calculator by a factor of 10 or so which is the ratio between active and total parameters in the model, I get results that are much closer to real world usage.

Re: Can I run AI locally?

#110

Post by philipkglass »

radlad wrote: post #104
mark_l_watson wrote: post #78

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 l…

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.

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