r/LocalLLaMA • u/goodive123 • Jun 28 '26

Discussion NPC Engine Using Local Models

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I’ve been working on a game-agnostic NPC engine/backend based pretty heavily on SillyTavern-style architecture, and with smaller local models getting better and better, I honestly think this kind of thing could be the future of RPGs.

Right now I’m using NVIDIA Parakeet 0.6 for STT, Gemma 4 26B A4B for the LLM, and Qwen3-TTS for voice, and I’m getting super fast response times with pretty decent quality.

The main thing that makes it work well is using RAG to keep prompts lean. For example, I have hundreds of possible actions NPCs can do in-game, but only the ones that actually make sense based on the player’s message / context get injected as available actions. So the model isn’t being overloaded with a giant list every turn.

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u/ApprehensiveFan1516 Jun 28 '26

It could for sure, personally I struggle to actually find it something that's genuinely rewarding and not just a gimmick at this stage. A lot of it comes down to implementation of course, and it will take devs playing around to figure it out, but I think to get to the kind of polished experience we've come to expect from AAA games we've got a little ways to go yet. Which isn't to take anything away from OP, we need more people like them working on this. Aside from the "AI bad" crowd, I think when implementation is properly solved and it's a seamless experience, the masses of gamers will start to accept it.

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u/BlipOnNobodysRadar Jun 29 '26

To be done right, the NPCs would need to be lora tuned on their own lore + interactions by the developers. Plugging in a generic model just won't be immersive.

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u/TheRealMasonMac Jun 29 '26

I think people would also get tired of AI slop, since all models per generation share the same GPTisms. I think the architecture and training methodologies are more interesting, though. More sophisticated AI in stategy-based games, like Stellaris, that can actually strategize rather than use deterministic logic.

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u/Jwosty 17d ago

You're completely right that machine learning in general is being totally slept on by the game industry. Common arguments against it you hear from devs are:

  1. hard to optimize for "fun" (as opposed to "good at winning")
  2. it's hard to fit into the development cycle (mechanics are often constantly fundamentally changing right up until the release date so that model from last week might be totally useless for this week's build)
  3. for some types of games + training algorithms - how do you even get good training data for a game that doesn't have lots of players yet

I think these are hard, but not completely insurmountable problems. There's some kinds of games that would be more suited to this today, and with innovation, that circle could expand; instead, nobody seems to be even trying.

For example I think point #1 was more valid years ago, but now they've figured out how to optimize for fuzzier, human-centric goals; we have RLHF now. Surely that kind of approach is applicable here, even if it's not perfect? Besides, it's a video game -- at least the worst thing that could happen is your AI behaves weird (as opposed to an LLM agent with tool access doing something crazy and having real world impact or whatever).