Had a question on the MoE: in kimmi-K2/model.py, the router does torch.topk(..., k=self.num_experts) while every other MoE uses k=self.top_k. The ctor's top_k=8 is never stored either.. Is that intentional dense routing or should that be self.top_k?
Somehow I expected inference engines are generic LLM runtimes that can execute any weight.
So, to get this right.
Someone trains a model.
They release the weights and a reference implementation of the model architecture.
Then a provider has to host this model either by running inference via the reference implementation, an open source implementation, or build their own.
Does this mean, providers don't just differ in quantisation and configuration, but also in inference engine implementation?
Most of it is pretty standard, since not that many different layers and primitives are used in LLM architectures, but once in a while something new comes along that needs more effort. MoEs are one example, they are sparse and allow for completely different inference patterns, which takes a while to figure out.
Last year I started a blogpost series about this topic (that I hope to update some time). I start from a minimal gpt implementation by Karpathy and build the engine around it, you might like it: https://pieter.ai/blog/2025/nanogpt-inference/
Some providers also have to implement their own engines, e.g., Cerebras has their own inference serving stack for their wafer-scale chips, as does Google for their TPUs (XLA compiler).
Most of the OSS models follow the same architecture which is Llama +- a few things, so it wasn't too hard for people to make it work.
OpenArch is a collection of these implementations, including Llama, Qwen, DeepSeek, Gemma, Kimi, GPT-OSS and others.
The goal is to keep the code readable and useful as a reference when going from the paper to an actual implementation.
Would be interested in feedback from people working on model architecture and training.
This is excellent for understanding. I'm having some trouble to get into understanding - pytorch is for me the RL which is used as gym/training. There I can chose ppo, dnq and other agents to perform some predefined actions in a predefined gym/world.
The repo you are showing - I really have problems to get it into RL understanding of mine. What's the gym? What are the agents. Can it be used to train that models with pytorch?
Sorry for the noob question. Papers are overwhelming my noob brain.
Your comment is a little unclear, so it‘s hard to parse your exact question. But it seems you are conflating 3 things, PyTorch, RL and Gym/Training (?).
– PyTorch is a framework which lets you define neural network models.
– RL is a collection of methods to train neural networks (change the network parameters to improve its performance).
– An RL-Gym is a framework to apply the neural networks to some problem. This lets you collect the data necessary to later use the methods of RL to train your model.