[FezzikTheGiant]

Are they able to run at a good speed? I'm just wondering what the economics would look like if I want to create agents in my games. I don't think many are going to be willing to get with usage based / token based pricing. That's the biggest roadblock with building LLM-based games right now.

Is there a way to reliably package these models with existing games and make them run locally? This would virtually make inference free right?

What I think is, from my limited understanding about this field, if smaller models can run on consumer hardware reliably and speedily that would be a game changer.

[verdverm]

> Are they able to run at a good speed?

Not on most consumer computers, which likely lack a dedicated GPU. My M2 struggles (only thing that makes it warm) with a 7B model, but token speed is unbearable. I switched to remote APIs for the speed.

If you are targeting gamers with a GPU, the answer may change, but as others have pointed out, there are numerous issues here.

> This would virtually make inference free right?

Yes-ish, if you are only counting your dollars, however it will slow their computer down and have slow response time, which will impact adoption of your game.

If you want to go this route, I'd start with a 2B sized model, and not worry about shipping it nicely. Get some early users to see if this is the way forward.

I suspect that remote LLM calls with sophisticated caching (cross user / convo / pre-gen'd) is something worth exploring as well. IIRC, people suspected gtp3-turbo was caching common queries and avoided the LLM when it could, for the speed

[sharpshadow]

You could also ship a couple of them and let the game/user choose which one to run depending on the hardware.

[FezzikTheGiant]

This is something I was considering as well - thanks

[imtringued]

There isn't. For games you would need vLLM, because batch size is more important than latency. Something that people don't seem to understand is that an NPC doesn't need to generate tokens faster than its TTS can speak. You only need to minimize the time to first token.

[alexvitkov]

The biggest roadblock is not running the model on the user's machine, that's barely an issue with 7B models on a gaming PC. The difficulty is in getting the NPC to take interesting actions with a tangible effect on the game world as a result of their conversation with the player.

[FezzikTheGiant]

The generative agents paper takes a pretty decent shot at this I think

[Isuckatcode]

Here [1] is a reference to the token/sec of Llama 3 on different apple hardware. You can evaluate if this is an acceptable performance for your agents. I would assume the token/sec would be much lower if the LLM agent is running along the side as the game would also be using a portion of the CPU and GPU. I think this is something that you need to test out on your own to determine its usability.

You can also look into lower parameter models (3B for example) to determine if the balance between accuracy and performance fits under your usecase.

>Is there a way to reliably package these models with existing games and make them run locally? This would virtually make inference free right?

I don't have any knowledge on game dev so I can comment on this but yes, packaging it locally would make the inference free.

[1] https://github.com/ggerganov/llama.cpp/discussions/4167

[FezzikTheGiant]

Thanks! This is helpful. I was thinking about the phi models - those might be useful for this task. Will look into how those can be run locally as well

[jlokier]

I just ran phi3:mini[1] with Ollama on an Apple M3 Max laptop, on battery set to "Low power" (mentioned because that makes some things run more slowly). phi3:mini output roughly 15-25 words/second. The token rate is higher but I don't have an easy way to measure that.

Then llama3:8b[2]. It output 28 words/second. This is higher despite the larger model, perhaps because llama3 obeyed my request to use short words.

Then mixtral:8x7b[3]. That output 10.5 words/second. It looked like 2 tokens/word, as the pattern was quite repetitive and visible, but again I have no easy way to measure it.

That was on battery, set to "Low power" mode, and I was impressed that even with mixtral:8x7b, the fans didn't come on at all for the first 2 minutes of continuous output. Total system power usage peaked at 44W, of which about 38W was attributable to the GPU.

[1] https://ollama.com/library/phi3 [2] https://ollama.com/library/llama3 [3] https://ollama.com/library/mixtral

[smarm52]

Well since OP doesn't seem to want to: Thank you for your response.

I came across this thread while doing some research, and it's been helpful.

(I hate how common Tragedy of the Commons is. =/)

[FezzikTheGiant]

What? chill out buddy - there's such a thing as timezones I was just sleeping