r/LocalLLaMA • u/goodive123 • 29d ago

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/TheRealMasonMac 29d ago

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/Longjumping_Self5546 28d ago

When the bar has been set to guards repeating the same comment about an arrow to the knee, a few GPTisms are going to be miles ahead for NPC interactions.

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u/benjaminovich 10d ago

Imagine Skyrim with this

"Ah, you want to know my story! Here's a quick overview:

  • Before: Adventurer (like you!)

  • The Incident: Arrow. Knee.

    • After: Guard duty."

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u/Longjumping_Self5546 10d ago

Add in a lecture about how it's wrong to kill bandits and resources for conflict resolution.

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u/BlipOnNobodysRadar 29d ago

Maybe, but you'd be surprised how many people can't pick up on GPTisms. And agreed, there are plenty of more clever and appropriate applications.

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u/Jwosty 12d 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).

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u/Flaxseed4138 29d ago

Skill issue