Pretty much everything has an API :P

ollama is OK because its easy and automated, but you can get higher performance, better vram efficiency, and better samplers from either kobold.cpp or tabbyAPI, with the catch being that more manual configuration is required. But this is good, as it “forces” you to pick and test an optimal config for your system.

I’d recommend kobold.cpp for very short context (like 6K or less) or if you need to partially offload the model to CPU because your GPU is relatively low VRAM. Use a good IQ quantization (like IQ4_M, for instance).

Otherwise use TabbyAPI with an exl2 quantization, as it’s generally faster (but GPU only) and much better at long context through its great k/v cache quantization.

They all have OpenAI APIs, though kobold.cpp also has its own web ui.

Uh, depends on your hardware and model, but probably TabbyAPI?

Text-generation-webui is cool, but also kinda crufty. Honestly a lot of the stuff is holdovers from what’s now ancient history in LLM land, and it has (for me) major performance issues at longer context.

I have an old Lenovo laptop with an NVIDIA graphics card.

@Maroon@lemmy.world The biggest question I have for you is what graphics card, but generally speaking this is… less than ideal.

To answer your question, Open Web UI is the new hotness: https://github.com/open-webui/open-webui

I personally use exui for a lot of my LLM work, but that’s because I’m an uber minimalist.

And on your setup, I would host the best model you can on kobold.cpp or the built-in llama.cpp server (just not Ollama) and use Open Web UI as your front end. You can also use llama.cpp to host an embeddings model for RAG, if you wish.

This is a general ranking of the “best” models for document answering and summarization: https://huggingface.co/spaces/vectara/Hallucination-evaluation-leaderboard

…But generally, I prefer to not mess with RAG retrieval and just slap the context I want into the LLM myself, and for this, the performance of your machine is kind of critical (depending on just how much “context” you want it to cover). I know this is !selfhosted, but once you get your setup dialed in, you may consider making calls to an API like Groq, Cerebras or whatever, or even renting a Runpod GPU instance if that’s in your time/money budget.