Ollama is awesome. I am part of a team building a code AI application[1], and we want to give devs the option to run it locally instead of only supporting external LLMs from Anthropic, OpenAI, etc. Those big remote LLMs are incredibly powerful and probably the right choice for most devs, but it's good for devs to have a local option as well—for security, privacy, cost, latency, simplicity, freedom, etc.
As an app dev, we have 2 choices:
(1) Build our own support for LLMs, GPU/CPU execution, model downloading, inference optimizations, etc.
(2) Just tell users "run Ollama" and have our app hit the Ollama API on localhost (or shell out to `ollama`).
Obviously choice 2 is much, much simpler. There are some things in the middle, like less polished wrappers around llama.cpp, but Ollama is the only thing that 100% of people I've told about have been able to install without any problems.
That's huge because it's finally possible to build real apps that use local LLMs—and still reach a big userbase. Your userbase is now (pretty much) "anyone who can download and run a desktop app and who has a relatively modern laptop", which is a big population.
I'm really excited to see what people build on Ollama.
(And Ollama will simplify deploying server-side LLM apps as well, but right now from participating in the community, it seems most people are only thinking of it for local apps. I expect that to change when people realize that they can ship a self-contained server app that runs on a cheap AWS/GCP instance and uses an Ollama-executed LLM for various features.)
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Ollama is awesome. I am part of a team building a code AI application[1], and we want to give devs the option to run it locally instead of only supporting external LLMs from Anthropic, OpenAI, etc. Those big remote LLMs are incredibly powerful and probably the right choice for most devs, but it's good for devs to have a local option as well—for security, privacy, cost, latency, simplicity, freedom, etc.
As an app dev, we have 2 choices:
(1) Build our own support for LLMs, GPU/CPU execution, model downloading, inference optimizations, etc.
(2) Just tell users "run Ollama" and have our app hit the Ollama API on localhost (or shell out to `ollama`).
Obviously choice 2 is much, much simpler. There are some things in the middle, like less polished wrappers around llama.cpp, but Ollama is the only thing that 100% of people I've told about have been able to install without any problems.
That's huge because it's finally possible to build real apps that use local LLMs—and still reach a big userbase. Your userbase is now (pretty much) "anyone who can download and run a desktop app and who has a relatively modern laptop", which is a big population.
I'm really excited to see what people build on Ollama.
(And Ollama will simplify deploying server-side LLM apps as well, but right now from participating in the community, it seems most people are only thinking of it for local apps. I expect that to change when people realize that they can ship a self-contained server app that runs on a cheap AWS/GCP instance and uses an Ollama-executed LLM for various features.)
[1] Shameless plug for the WIP PR where I'm implementing Ollama support in Cody, our code AI app: https://github.com/sourcegraph/cody/pull/905.