Are there any agentic models like this that would work for controlling input in arbitrary video games? I've been wanting to have an AI play Kerbal Space Program because I think it would just be pretty hilarious.
I might suggest looking at Alibaba's open source AgentEvolver. It doesn't specifically target video games, but it's an agentic system designed around a more OODA loop evolutionary system than the kind of train/inference system, has potential, could be exciting to see.
I like how they classifythr sub problems of their work. Environment/ self questioning -> task / self questioning -> trajectory / self evaluation. OODA-esque.
How much VRAM would this require, if I would want to run this locally?
I bought a 12GB Nvidia card a year ago. In general I'm having a hard time to find the actual required hardware specs for any self hosted AI model. Any tips/suggestions/recommended resources for that?
One quick way to estimate a lower bound is to take the number of parameters and multiply it with the bits per parameter. So a model with 7 billion parameters running with float8 types would be ~7 GB to load at a minimum. The attention mechanism would require more on top of that, and depends on the size of the context window.
You'll also need to load inputs (images in this case) onto the GPU memory, and that depends on the image resolution and batch size.
Are there any agentic models like this that would work for controlling input in arbitrary video games? I've been wanting to have an AI play Kerbal Space Program because I think it would just be pretty hilarious.
https://deepmind.google/blog/sima-2-an-agent-that-plays-reas...
(not a local model)
I might suggest looking at Alibaba's open source AgentEvolver. It doesn't specifically target video games, but it's an agentic system designed around a more OODA loop evolutionary system than the kind of train/inference system, has potential, could be exciting to see.
I like how they classifythr sub problems of their work. Environment/ self questioning -> task / self questioning -> trajectory / self evaluation. OODA-esque.
https://arxiv.org/abs/2511.10395 https://github.com/modelscope/AgentEvolver with thanks to Sung Kim who has been a great feed https://bsky.app/profile/sungkim.bsky.social/post/3m5xkgttk3...
How much VRAM would this require, if I would want to run this locally?
I bought a 12GB Nvidia card a year ago. In general I'm having a hard time to find the actual required hardware specs for any self hosted AI model. Any tips/suggestions/recommended resources for that?
One quick way to estimate a lower bound is to take the number of parameters and multiply it with the bits per parameter. So a model with 7 billion parameters running with float8 types would be ~7 GB to load at a minimum. The attention mechanism would require more on top of that, and depends on the size of the context window.
You'll also need to load inputs (images in this case) onto the GPU memory, and that depends on the image resolution and batch size.
* fine tuned Qwen-7B
So.. the tables are really turning?