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I see a couple comments comparing llama.cpp and Ollama, and I think both have utility for different purposes. Having used both llama.cpp (which is fantastic) an
by zora_goron 2y ago
I see a couple comments comparing llama.cpp and Ollama, and I think both have utility for different purposes. Having used both llama.cpp (which is fantastic) and Ollama, a couple things that I find valuable about Ollama out-of-the-box --
- Automatically loading/unloading models from memory - just running the Ollama server is a relatively small footprint; every time a particular model is called it is loaded into memory, and then unloaded after 5 mins of no further usage. It makes it very convenient to spin up different models for different use-cases without having to worry about memory management or manually shutting down those tools when not in use.
- OpenAI API compatibility - I run Ollama on a headless machine that has better hardware and connect via SSH port forwarding from my laptop, and with a 1 line change I can reroute any scripts on my laptop from GPT to Llama-3 (or anything else).
Overall, at least for tinkering with multiple local models and building small, personal tools, I've found the utility:maintenance ratio of Ollama to be very positive -- thanks to the team for building something so valuable! :)
- benreesman 2y agoI’m pretty passionate about the space and I’ve seen good diffs from the ‘Ollama’folks, they’ve pushed the ggerganov repo forward in some ways. I’m a bit unsettled about what to me feels like an ambiguous posture on commercial activity. I’ve got no issue with folks doing open-source type stuff to make money, a lot of good code gets written that way. But I’ve learned the hard way that if a project feels kinda squirrelly it’s wise to keep one’s ears up. I still build ggerganov main from source and type out the five parameters.
- etc-hosts 2y agoyou can even immediately unload the model from memory: curl http://localhost:11434/api/generate -d '{"model": "MODELNAME", "keep_alive": 0}'