cortex VS nnl

Jan incorporates a lightweight, built-in inference server called Nitro. Nitro supports both llama.cpp and NVIDIA's TensorRT-LLM engines. This means many open LLMs in the GGUF format are supported. Jan's Model Hub is designed for easy installation of pre-configured models but it also allows you to install virtually any model from Hugging Face or even your own.

I'd like to see a comparison to nitro https://github.com/janhq/nitro which has been fantastic for running a local LLM.

Look... I appreciate a cool project, but this is probably not a good idea.

> Built on top of the cutting-edge inference library llama.cpp, modified to be production ready.

It's not. It's literally just llama.cpp -> https://github.com/janhq/nitro/blob/main/.gitmodules

Llama.cpp makes no pretense at being a robust safe network ready library; it's a high performance library.

You've made no changes to llama.cpp here; you're just calling the llama.cpp API directly from your drogon app.

Hm.

...

Look... that's interesting, but, honestly, I know there's this wave of "C++ is back!" stuff going on, but building network applications in C++ is very tricky to do right, and while this is cool, I'm not sure 'llama.cpp is in c++ because it needs to be fast' is a good reason to go 'so lets build a network server in c++ too!'.

I mean, I guess you could argue that since llama.cpp is a C++ application, it's fair for them to offer their own server example with an openai compatible API (which you can read about here: https://github.com/ggerganov/llama.cpp/issues/4216, https://github.com/ggerganov/llama.cpp/blob/master/examples/...).

...but a production ready application?

I wrote a rust binding to llama.cpp and my conclusion was that llama.cpp is pretty bleeding edge software, and bluntly, you should process isolate it from anything you really care about, if you want to avoid undefined behavior after long running inference sequences; because it updates very often, and often breaks. Those breaks are usually UB. It does not have a 'stable' version.

Further more, when you run large models and run out of memory, C++ applications are notoriously unreliable in their 'handle OOM' behaviour.

Soo.... I know there's something fun here, but really... unless you had a really really compelling reason to need to write your server software in c++ (and I see no compelling reason here), I'm curious why you would?

It seems enormously risky.

The quality of this code is 'fun', not 'production ready'.

I did roughly the same thing in one of my hobby project https://github.com/fengwang/nnl. But in stead of using SSD, I load all the weights to the host memory, and while inferencing the model layer by layer, I asynchronously copy memory from global to shared memory in the hope of better performance. However, my approach is bounded by the PCI-E bandwidth.