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It depends on the exact specs of your old laptop. Especially the amount of RAM and VRAM on the graphics card. It’s probably not enough to run any reasonably smart LLM aside from maybe Microsoft’s small “phi” model.

So unless it’s a gaming machine and has 6GB+ of VRAM, the graphics card will probably not help at all. Without, it’s going to be slow. I recommend projects that are based on llama.cpp or use it as a backend, for that kind of computers. It’s the best/fastest way to do inference on slow computers and CPUs.

Furthermore you could use online-services or rent a cloud computer with a beefy graphics card by the hour (or minute.)

What I’m using is Text Generation WebUI with an 11B GGUF model from Huggingface. I offloaded all layers to the GPU, which uses about 9GB of VRAM. With GGUF models, you can choose how many layers to offload to the GPU, so it uses less VRAM. Layers that aren’t offloaded use system RAM and the CPU, which will be slower.

Probably better to ask on [email protected]. Ollama should be able to give you a decent LLM, and RAG (Retrieval Augmented Generation) will let it reference your dataset.

The only issue is that you asked for a smart model, which usually means a larger one, plus the RAG portion consumes even more memory, which may be more than a typical laptop can handle. Smaller models have a higher tendency to hallucinate - produce incorrect answers.

Short answer - yes, you can do it. It’s just a matter of how much RAM you have available and how long you’re willing to wait for an answer.

The easiest way to run local LLMs on older hardware is Llamafile https://github.com/Mozilla-Ocho/llamafile

For non-nvidia GPUs, webgpu is the way to go https://github.com/abi/secret-llama

You would need 24gb vram card to even start this thing up. Prolly would yield shiti results

I watched NetworkChucks tutorial and just did what he did but on my Macbook. Any recent Macbook(M-series) will suffice. https://youtu.be/Wjrdr0NU4Sk?si=myYdtKnt_ks_Vdwo

I’m in the early stages of this myself and haven’t actually run an LLM locally but the term that steered me in the right direction for what I was trying to do was ‘RAG’ Retrieval-Augmented Generation.

ragflow.io (terrible name but good product) seems to be a good starting point but is mainly set up for APIs at the moment though I found this link for local LLM integration and I’m going to play with it later today. https://github.com/infiniflow/ragflow/blob/main/docs/guides/deploy_local_llm.md

While you can run an llm on an “old” laptop with an Nvidia GC it will likely be really slow. Like several minutes to much much longer slow. Huggingface.co is a good place to start and has a ton of different LLMs to choose from that range from small enough to run on your hardware to ones that won’t.

As you are a teacher you know that research is going to be vital to your understanding and implementing this project. There is a plethora of information out there. There will not be a single person’s answer that will work perfectly for your wants and your hardware.

When you have figured out your plan and then run into issues that’s a good point to ask questions with more information about your situation.

I say this cause I just went through this. Not to be an ass.