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InfluxDB
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Using this format likely makes distribution a lot easier as they can just use an existing OCI registry for hosting the images, and you can use existing push/pull primitives. OCI images are often used nowadays for things besides docker images if you have a format (like a layered filesystem) that is nicely content-addressable.
The default registry for Ollama is https://ollama.ai/library (I don't know about the API endpoint). I'm not sure if/how you can actually use it with another registry endpoint.
When people usually say that something "uses Docker" that usually involves utilizing the Docker daemon with it's sandboxing, networking, etc.. Ollama doesn't do or need that as it can just do inference natively based on GGUF files (+ the other configuration that comes as part of their model file).
However, as you have noticed, it does borrow heavily from Docker. E.g. apart from the registry mechanism its Modelfile heavily mimics a Dockerfile.
[0]: https://github.com/jmorganca/ollama/blob/aabd71aede99443d585...
https://github.com/second-state/WasmEdge-WASINN-examples/tre... These are the speed of different hardwares. You can rent GPU, which will be much faster
I really like the post that they mention (https://www.secondstate.io/articles/fast-llm-inference/). The reasons for avoiding python all resonate with me. I'm excited to play with WASI-NN (https://github.com/WebAssembly/wasi-nn) and that rust code is very readable to load up a GGUL model.