So What? How To Get Started With Small Language Models

Happy Thursday, everyone. Welcome to So What, the Marketing Analytics and Insights Live Show. Uh, Chris and John here this week. Katie is off uh searching for uh yet another real life law and order SVU episode. For those who don't know the reference, last year Katie was uh making her annual pilgrimage to New York City and saw, of course, uh the event with uh Luigi Manzioni and uh and everything that happened there.

And and so uh she's back this week uh in New York pursuing that. Uh this week we're talking about small language models, the uh little siblings to large language models, the kind that power chat GPT, Google Gemini, Claude, etc. So before we get started, John. Uh what do you know about small language models? You know, it's everything that you've thrown out in previous episodes of stuff we've kicked around.

I mean, but I love the idea that instead of firing your data off to who knows where to be used for who knows what, you know, there's a lot of these models that you can just install on a local machine and you get the same functionality, but you get to hang on to the data. But the big thing for me, I get that and have played around with that a little bit as far as setting up a model and just having it work. But I really want to hear about what you've got going as far as, you know, okay. So you've got it set up, but now what do you do? Like, how do you plug it in?

I can see, yeah, it's great that you can now work on your desktop and not have to worry about you know divulging private info or you know something going horribly wrong. But I would love to hear more about okay. So you've got it set up on your machine. Here's how to plug it into some other stuff to make it more useful. That's um what I'm really hoping we can dig into a bit.

Nice. So let's let's clarify. There's a difference between a small language model and a local AI model. Local models run in your infrastructure. So on your laptop, on your phone, in your server room.

They don't have to be small. So, for example, you can you can, if you have $50,000 worth of hardware, uh download Deep Seek uh DPC 3.2 and run it in your infrastructure completely private, totally safe, um, but not small. Uh a small model, there's no universal agreement as to what's constitutes small. Uh, however, the the general rule of thumb that I use is if you can run it on a laptop uh and not be connected to the internet, that is small. And again, that depends on the the kind of laptop you have.

If you have like I, you know, Trust Insights pays for me to have the top-of-the-line MacBook Pro, that can run pretty decently sized models, not deep seek, but uh it can run the the newest versions of Quen3, et cetera. If you have your average corporate laptop that can basically run a browser and Microsoft Office, and that's it, uh, you are much more constrained because all language models require memory. Uh, they specifically require video memory. And the more video memory your computer has, the the bigger the models you can run. The exception to that rule is Macs.

Macs don't have separate video memory. They it's all in one. So however much memory your your Mac has after you've turned it on and turned on whatever you want to be, you know, running elsewhere, whatever's left over, that gives you the budget. So that's the difference. Small language models can be also in the cloud.

So there are some really good providers out there that allow you to run in the in their clouds. So a couple of ones that are pretty well known. Grok with a Q, nothing to do with Elon's shop. Um this is a shop that is known mainly for being having the fastest uh models where you run some of the these uh models that are available in here, they run crazy fast. And and they have things like Quen3 in here, they have Kimi K2, they have some you know uh these are the this one is not small.

Um, another one that is fairly well known, Deep Infra. This is a company that allows you to run, uh they have hundreds of different models, big and small and size. So if you wanted to use small models, uh you can do it on your hardware, or you can rent someone else's hardware. The reason you would rent someone else's hardware is a company like Grok, for example, has a zero data retention API. So you could if your machine, like if if you work at you know Acme Megalithic Corporation, and they've given you the cheapest possible piece of hardware that uh that you know you can't even run solitaire on it.

Uh you're not running a local model on it. So you would use if you want to use a small model, you would do it in the cloud. And the reason you would you would run a small model is mainly cost at that point, cost and privacy. These small models, when you run them, they they run much, much faster because they require much less hardware to run. That also means that they are much more sustainable in terms of environmental impact because they require less compute, right?

If you if you can take a bicycle to the store, uh you don't need to drive the Ferrari, uh, you will save uh you'll save a little bit of carbon. That's interesting. So the small hosted model, it it seems like that would be a better application if you were trying to build something. I mean, the idea that it would be up all the time, you don't have to dedicate your own machine to it. Um, is there what's the remaining reasons then for wanting to run a small model locally?

I mean, it if privacy is of top concern is mission critical. Is that the yeah, privacy is a big one. Uh where you're like, you know what, we have data that cannot leave our network, right? It cannot leave or cannot even leave this machine. Um you would you that's that's one reason to run it locally.

Another is cost savings, particularly if you're doing like RD or you're building like your first beta tests, you might say, you know, we're gonna use Meta Llama in uh Llama 4 Scout in the cloud eventually, but while we build and debug this thing, there's no reason to run up a huge bill um or a modest size bill when we can run it locally, it'll be slower. But while we work out all the bugs of what we're working on, there's no reason to to spend that money. Very cool. All right, yes. So that isn't just that does kind of change the thing.

I like the idea of if you're kind of in the lab or in early development, yeah, you run it local, but then once you get to a point where you it you want to do maintenance and not have to babysit, I could see throwing the cloud would be a great way to go. Yeah. Uh the other thing is if if you want to run offline. So a big one for me because I travel so much, uh, Quen 3 Coder is one of the best small models for coding. It's still not as smart.

Uh Ethan Moloch said this uh recently. Uh open models, which is the ones we're talking about, small language models and and local AI, uh typically open models. Uh they lag behind state of the art by about eight months. So today's Quen 3 is about as smart as where, say like GPT 401 was back in April. It's it's not the the cream of the crop.

For a lot of tasks, it doesn't have to be. Uh so uh but like when I get on an airplane, if I'm like on on you know airline that shall not be named, uh, and the internet is either not available, highly unstable, or is like 280 dollars for 15 minutes. Whatever the cases, like you know what, I'm just gonna use the local model. I'm I'm gonna run a small language model on my machine to get some stuff done. Yeah, that's a great point too.

I I'm just like presuming that yeah, internet and current are ubiquitous, which is not the case in a lot of places. Exactly. Yeah, even if you're going now, we've all stayed in that hotel that has you know internet speeds that rival nineteen ninety-nine. So uh Katie is saying hello from the train. I uh uh okay, I'm I'm okay if you're on the train.

You're not um interrupting tourist time, but uh well, safe ride with on the uh on the rails there. Exactly. So our our first two things are uh privacy is cost savings, offline capability, sustainability, the reasons that you would consider small language models, and small language models can run on your hardware or someone else's hardware. The what makes them useful is that they are small so that they consume fewer resources. Now, here's the catch.

The smaller a model is, the dumber it is in terms of knowing how to how much knowledge it contains. Because a lot of models, you know, you take a model like Gemini 3. We don't know how big it is, but we you know, you can hazard a guess it's probably like three to four trillion parameters, uh, which is a bookshelf that goes of of books that goes around the equator like eight times. Compare that to a model like uh you know, Quen 3, that's an 80 billion parameter model. So it's literally a hundred times smaller, which means it contains a hundred times less information in it.

How about as far as when you configure those? Are you able to select kind of what data you want? Or it's just no, this is like what's been released and what's in there for this version. For the purposes that most folks are going to be using these things for, you get what you get. Um, there are a lot of what are called fine tunes where someone else has tuned it to do a specific thing, but for the most part, for the average business user, uh, it is what it is.

And that brings us to these work best when you bring the data. So if you are using a small language model and you're like, write a blog post about B2B marketing, it's going to give you such slop, such such crap because it has even less knowledge than chat GPT. So it's just gonna come up with this this very mealy mouthed, you know, boring AI. You see this a lot. You know all those those annoying comments on LinkedIn that are you know are 100% machine generated.

Those are on the back end, those bots use small language models because of the cost. If you're going to send a hundred thousand robocomments, you don't want to be spending you know a dollar per comment with OpenAI. Like, you know what, I'm going to use Llama for Scout and cost me a penny a comment, and you go out and you spam LinkedIn and then we all hate you. Right. Well, and then the thing with that is that it's um, you know, you know that they're doing that to get around all the the fact there's no API and they're actively discouraging automation stuff.

So it's that's more criminal activity. Exactly. Now, if you want to run small models in your machine, the first thing you have to know is how much memory do you have available. If you're on a PC, you have to know uh how much video memory you have available, and that's usually in your Windows settings. It's been a really long time since I've used Windows, so I don't actually know the answer to how to find that.

Um, and if you're on a Mac, uh you can open up your activity monitor and look at how much memory is free. So how much memory is available. That will tell you how much capacity you have. Then comes the challenging part of figuring out which of the 2.3 million models should you use just a few choices. Um there is a fantastic uh tool that I use.

Uh that's it's uh it's just a uh a website comparison tool uh called artificial analysis. Um we have no relationship with them, but one of the things that they've got is the ability to choose from all these different model families. So I've put up the the biggest most well-known families: Alibaba's Quen, uh ByteDance's seed, Meta's Llama, uh Mistral's uh various models, Microsoft's fee, Google's Gemma, and LG, the the uh refrigerator company, uh LG's X01. And so this is sort of a comparison intelligence-wise about what these models are good at. And this particular site creates kind of a nice average of all the different tests.

And so you can see here uh we see Quen3 Next is sort of the newest version of this. It scores a 54 on their index. 65 on this index is a human PhD to just a level set, and uh 20 is like you know, face rolling moron. So you see the the very smallest version of Google Gemma, face rolling moron, uh Microsoft Fee, face rolling moron, uh fee uh fee four, the regular version, slightly above face rolling moron, uh Quen, Alibaba, uh Byte Dance, uh, LG, and Mistraw all score pretty decently. Now, if I add in just for comparison, uh if we add in regular GPT-5.1, which is the current version of Chat GPT, uh, and we and I put in the non-thinking version, find it here.

Five one. I'll just put in so that's where so 70 is where the the smartest version of Chat GPT is today. So you can see there is a big difference compared to, say, you know, Microsoft fee, but at the same time, it's not vastly different than Quen3. So what I would suggest for people who want to figure out which even model family to use, use a tool like this and say, here's what we're used to. Knowing that we can't do generation, knowing that we probably can't ask fact-based questions of the model itself, what's going to be closest to what we're used to, which in this case is either gonna be ByteDance or Quen3.

Those two. Those are both Chinese companies. Quen is made by Alibaba, the e-commerce giant, and Seed is made by ByteDance, the manufacturers of TikTok. Probably worth mentioning at this point. When you use a small language model that is on your hardware or a provider of your choice, it is safe to use, even if it's made by a model maker that's outside your jurisdiction.

A lot of people have concerns about using tools, you know, models like Deep Seek and stuff because when you use their version of it on their infrastructure, hosted in the People's Republic of China, yeah, their privacy policy says you have none. So be aware of that. When you take the model and you put it on your computer, it's now not part of their systems. And so it is then safe to use. The one that I recommend for most people to use these days is a tool called LM Studio.

LM Studio is a local AI system. It's available for Mac and Windows, and it's sort of an all-in-one that allows you to download models, run them on your computer, and then also use them if you want to use them for programming. And what's really nice about it is that uh you don't have to go copying and pasting and finding model names and stuff like that. It's much easier just to go into the tool and say, let's let's see what's available. So we were just looking at artificial analysis.

We know that the Quen family is probably gonna be the best for what we want to be doing. Um, and so we can see here's all the recommended ones for my computer that I could use. Uh and they this connects to uh a website called Hugging Face. And so it has all 2.3 million models if you wanted them. So I've downloaded uh in advance the show because these are big files.

Uh one of the things to you can pay attention to is you can see a how much disk space it takes up is about how much memory it's going to take up. So you need to budget for that. When if you've got 12 megabytes of video RAM and you look at like, oh, I I want this cool model. This looks great, 44 gigabytes. Well, you ain't mind on that.

It's not gonna fit. Um, but it is a it is it's nice and simple. Now, once you have it downloaded, you can start to do uh let's start a new chat. And Quent3 Coder is loaded. Now, this is this is a coding model, but it still can answer general questions.

Like uh, let's give it a terrible one. Uh, what are the best practices for B2B marketing and account based marketing? Right. Terrible prompt. But you can see how fast it is.

Like it spit out crap, but that spit out crap really fast. There's no delay in serving up the slot. Exactly. And if you think about this, is why you would run like this as the back end of like a LinkedIn comment bot. Because if you got to make 100,000 comments, you need it to be fast.

Um so this is one way to run these tools. Another way is if you're building something, like if you're using a tool like N8N, which we've covered on the on past episodes of uh the live stream, which you can see at uh trusted science.ai slash YouTube. If you're using NAN, you can use local models or small language models, either one in there. So let's say you went and got an account on Grok or Deep Infra. You could put that in to your N8N and say use this instead of Google Gemini or OpenAI.

And because the prices are so much cheaper and in some cases much, much faster, you can have a much more satisfactory experience with it because it is it's just so incredibly fast and inexpensive. You look like you think in there. Yeah, I know, I know. The wheels are definitely spinning on that. I'm trying to figure out how to put those pieces together.

So the idea is N8N it does have it compartmentalized too. You can just pick and choose the models that you want the thing to dig into as it goes. And now, can you have N8N point to um LM Studio though? Yeah, you can have it point to your local computer, you can have it point to a cloud provider, either one. So let's say I wanted to uh I'll put a start a manual transcript here.

Let's go with wonder if we can get it to transcribe a podcast. Let's give that a try. So let's read a file. Read write files from disk. I'm gonna read a file and call this users pen desktop live streaming.

And that'll be the directory we'll read from. Let me go ahead and actually make that folder. And I'll put an episode of marketing over coffee in there. So on my desktop, we've got this this folder. And now let's add in a some AI.

Use a basic language model. And we're gonna choose. I already have Grok set up. And we're gonna use MetaLama for Scout, which can listen to audio. So we'll go back up here and we'll give it a simple prompt that says transcribe this audio.

Terrible prompt. Don't do this for real. Um then we should be able to say let's go to a write to the file. Let's write our user CS pen desktop live stream transcript.md. So what this will do is this will talk to Grok, it should read the file and said, Oh, I expected by an I I expected uh I need to convert this.

Convert to file, convert to plain text. Let's make sure that's there. So that and I need to actually include the data, which would be helpful. Schema, we want the data. So what we've got here, what we should have here is our ability to bring this data in and give it to give the mp3.

Whoa. I actually played the show. Let's see, file size. Should just be file. All right.

In any event, uh that's how you would use one of those small language models. You would say, I want to pass this to the model, whether it's running on your computer or whether it's running in the cloud, and transcribe it. Now imagine if you were to put all 900 episodes of Market Air Coffee in that folder as MP3s. It could process all of them in one shot and just give you a huge, painfully large pile of transcripts. That's good, yeah.

And being able to plug that in just saves you all the hassle of trying to figure out how to work it, you know, put it up into the cloud somewhere. Exactly. Um, so that's one use case. Another use case that is super helpful, at least for me, is you can use these things in coding environments. So again, Quenthree Coder is the one that was we were referring to earlier.

And just because it's in a coding environment does not necessarily mean you're you always have to be writing code. You could be you know cleaning up transcripts and things like that, but they are also phenomenal coders. So uh there's pretty much everybody on the planet nowadays uses the uh tool called Visual Studio Code or a variant thereof, and in the way down in the bottom, you can connect your small language model, either cloud or local, depending on it, and you can start to you can give it a project to work on. So uh, and and it can actually start to write code. So if I go in to my VS Studio Code, I bring in my live stream folder, I make sure that I'm connected to thing, and let's just do something silly.

Let's make an HTML and CSS infographic about the importance of B2B marketing, right? Terrible prompt. Uh I'm gonna turn on plan mode, and it should be it should start talking to our low our the the small language model that we have locally. I should make sure it's actually turned on. Yep, there it goes.

It's working, it's thinking its way through it. And so as it's once it's done with its thinking, it will start its generation. And so this is how you would do this, like on a plane, once it's whoa. So here comes the plan, the design elements, and that looks good. Uh would you do I want to focus on anything particular?

Let's focus on ROI and measurable results, and now I can change into act mode, and it will literally start writing the code for me to make a nice HTML infographic. Now, when you use something like Google Gemini or ChatGPT, this is what's going on behind the scenes. But you're able to do this completely under your control. So if you want to make an infographic about with sensitive data, this you could do this and and not have to worry about leaking that information. So a real simple example, you might have say notes from your personal medical history, and you might want to put together a flowchart or a timeline of what's been going on with you.

And you don't want you don't feel comfortable handing that to a cloud provider. So you might say, you know what, let's make this, but you would you use a small language model that is either safely cloud hosted or local, and you can still have all the benefits of that AI with none of the privacy concerns. It's still right. What do you how long does something like this usually take? Is this something that should be able to crank out soon?

Or is it um so it it's it's writing HTML, so it could be a little while because it's it's having to think through what are all the the pieces that it needs to assemble this. Now, if we go back while it's while it's churning away, if we go back to what we were talking about earlier about small language models, right? We talked about the fact that they're processors. Uh one of the big things is that they're good at five of the seven use cases, they're good at extracting information. So if you can give them information, whoa, hey, look, our infographic just popped up.

It just fired out uh it just fired out. You know what? I don't hate that. It's not bad. Yeah, no, that's a solid web page, definitely.

Yeah, it's oh, it's even got little interactives and stuff like that. And cool. I mean, and that the cost to us for that was zero because it was churning away on my computer. I might say, you know what? Uh yep, hip round, that's fine.

Let's use uh tell you have to use the trust insights brand style guide. Let's see if we can get it to do that. Think that through. Uh going back to our review of why these things even matter. If you're providing data, they can do the first five use cases of AI really, really well.

So they can take data and extract it. Let's say here's some transcripts, extract out this information. Uh you've here's some some social media comments, classify them, positive or negative sentiment. Here's a conference call, summarize the you know what happened in the conference call. Here's a blog post, you know, rewrite it into a blog post using only emoji.

Um, here's a whole bunch of small data pieces, aggregate them and create you know a paper from from large pieces. In all these cases, you're bringing the data, and then they they are acting on it. So they're they're very good at that. Where a lot of people like to use generative AI is like asking questions like, hey, I've got this, you know, situation, what should I do about it? These small language models, because of their size, do not have enough knowledge.

So they're going to give you wildly wrong information. Uh that is uh not gonna be helpful. And it's the same is true for generation for making new stuff, like write a blog post about this thing. Out of the box, they have no ability to know what is true or not. So um, out of the box, do not use them for that.

Right, but it just seems like this is a no brainer for if you have huge piles, like you said, transcripts or surveys or you know, 500 PDFs of books that you want to churn through, like anything like that. That's the summarization and categorization is is where you can really make it work. Exactly. One of the uh use cases that I love small language models for is uh classification and renaming. So I use the Metallama 4 model, uh, which has uh multimedia support to load images, like all my entire library of screenshots on my computer that are all named screenshot 2025-12.

Whatever, you know. You have no idea what's in a screenshot. I say, look at this and rename these thousand screenshots into something that's actually in the image so that we know what the heck it is. I mean, that's a simple use case. Another one is academic papers.

When you download academic papers from places like archive.org, uh, that comes in with you know 2510. Uh 1174 v1 is the name of the file. You're like, that's totally unhelpful. What if you just take this PDF and you name it what the name of the paper is? And so the you know, any of these small language models can take the PDF if they're if they're capable of working with it, read it, find the title, and say, I'm gonna rename this file.

And because they have access to the local file system, they can do that. And suddenly your PDF folder, which is filled with you know, just tragic amounts of of uh unnamed stuff, is usable again. So any application where you want to be cleaning stuff up uh uh and just doing administrative things, so useful for that. Yeah, cleaning up that pile of graphics is just like a job you'd never get to. Exactly.

So this is now going through and applying our the Trust Insights brand standards uh because we told it what it was, uh, and now it can just go through and fix up the HTML and fix up the infographic and make it look you know less super generic and more appropriate. Let's see how it's coming along here. Oh, look, that it's it's still working, but that's that's already getting the right colors, it's already getting the right fonts, so substantially improved there. So the last thing is which models should you use? Um, the ones that I recommend out of the box.

If you want a general all-around model that is also good at coding, Quen3 Coder is very hard to beat. As long as you have the hardware to support it, it is such a good model. Um, if you're dealing with a lot of multimedia llama for Scout, uh is it you know, if you like, hey, I've got screenshots to rename, that's the model to use. If you want a model that writes really well, Ms. Strawl's small model is a shockingly good writer in terms of you know how a lot of people complain, oh, you can tell chat GPT wrote that, it's got you know M dashes and all that stuff.

Ms. Stroll as a as a literary model is quite good. And there's some there's some versions of it that are really good at things like fiction writing. If you want to generate images, uh there's a model called Flux 2 from Black Forest Labs. Uh that is absolutely phenomenal, is almost as good as Google's Nano Banana Pro.

It can run on your computer. And if you want a model that transcribes uh spoke the spoken word really well, NVIDIA's parakeet is is the best model. And Parakeet is so small it can run on almost any computer. So across the spectrum, that's what's available. If you want to see uh Flux 2, Flux 2 is just shockingly good.

Let's take a look at Flux 2 Pro. Uh let's see. Uh give me an interesting image prompt. Let's see what it can come up with here. Oh, let's see.

Um, how about a robot with an umbrella in Chicago? All right. A Terminator style robot uh with an umbrella on the streets of Chicago in the rain, carrying a deep dish pizza. Let's see what it can what it can come up with here. Um when you're using both local models and uh and cloud hosted models, uh you'll note that these things can take in inputs, and unlike uh the big the big cloud providers, you can set the level of safety.

So you can say, like, yeah, I want the this model to generate unsafe outputs. Um should you want to like you know what? That's not terrible. No, no, that's not bad at all. I mean, they nailed the pizza, they got the terminator.

I don't know where the pizza box lid is, but another uh vanishing thing. Yeah, and I gotta say, the the streets do resemble Chicago. I mean, that's your typical American uh thing. The traffic lights are a little funky back there. Uh, but yeah, in general, that's not bad.

And I like the fact that it put a leather shot on it for no reason. Right, right. The skin's burned off, but somehow the leather jacket survived. Exactly. Now, flux 2, you can run on your computer.

Uh, and you'll notice something uh that is somewhat concerning about this output. There's no watermarks on it. There's nothing that says this is AI, other than the fact that like, you know, there's there's those still those little weird weirdnesses, like the lettering on the pizza box is not quite right. Right. And the lighting is a little off.

Like the pizza's really well lit. Chicago paying their dues there. Exactly. Uh John Stewart would be evolved. Um however one of the the challenges with local with local models and with small language models and small any kind of model is uh they you have complete control, which is good and bad.

You have complete control, which also means you have to use it responsibly because it can generate things that you know the the big models that have a lot more censorship built in won't let you do. So to recap. We want to use these things uh when we want privacy or cost savings, be able to run them offline and be sustainable. Small language models are differentiated by their size. You can run them anywhere in the cloud, you can run them on your computer.

Whereas local models are based on where the model is run. All small language models can be run locally. Um not all local models can run on small hardware, like Deepseek, for example, because you need thousands of dollars worth of stuff. The models themselves make sure that you are providing the data. You are always you always need to provide the data uh and make sure you have enough memory for them.

And of the use case categories, uh, the ones they're really best at extraction, classification, summarization, rewriting, and synthesis. Question answering and generation can only be done with uh with data you provide. You cannot ask them questions for things that you didn't provide the information for. And you know, the the panoply of the five different ones that we use. So with that, where are you going to get started, John?

You know, I would definitely want to mess around with the image generation because I the baked in stuff in Adobe Photoshop has not really impressed me that much. It's done a lot of weird stuff. So I would like to see if I can do some more interesting stuff on that front. Um and then I yeah, I don't know. I think for most use cases, I'm still okay going to the web.

You know, I LM Studio is what I played with before and would keep going with, but it seems like I'd be you know better served by spending more time shining up my prompts than uh making sure that it's accessible offline. Yeah. You can see by the way, it just popped up the infographic again, so it is now revised it. Even got it got our logo in there, which I like. Uh I'm happy about that.

Uh the the act, the labels on these bars are still a little funky and things, and all 100% of this data is hallucinated. So there's that's that's another thing because bear that in mind. If you if you're like, oh, trust inside says that you know, uh B2B market has a 4.2 X RI. No, this is completely made up by a local model, by a small language model. But do please do not believe this.

This is this is factually untrue. Um, but you can see that when you're on that flight from Boston to San Diego and the internet's not great, you can still get quite a lot done. Yeah, and well, and you know, you've got the page, the HTMLs laid out right. Like it's no big deal to go in there and you know, set those stats to what they're supposed to be. You're just not having to go through the grunt work of getting that page laid out.

Exactly. Uh, if you'd like a copy of this, the the show notes infographic here, we're gonna put it in our free Slack group. Go to TrustInsit.ai slash analytics for marketers, and uh you'll be able to grab a copy of this this lovely thing. Um any final parting thoughts. Uh you know, just dig in there and start breaking stuff, you know, play with it, get to figure out what's going on.

Yeah, and again, remember you don't have to run small models locally. If you just don't want the hassle of it, you can sign up and with uh providers like Deep Infra uh and Grok with a queue, uh it is pay as you go. So you uh like with Deep Infra, uh I I believe I put $50 in, and uh even with you know generating images and stuff like that. I should see what is what is my uh what is my billing here. Uh I have currently used three cents, uh 48.69 cents remaining.

So you've got a balance. Yeah, I've got uh this is like oh you you've used a dollar uh since you signed up with us a month or like six months ago, uh, because that's how cheap local models are. That's great. Yeah, that's you know, it's just not even entering the equation as far as headache. Exactly.

All right, folks, that's gonna do it for this episode of So What? Uh, thanks for tuning in, and we will tune see you next week for the last and final so what of 2025 before we are on holiday break. So make sure you tune in. I have no idea if there's going to be shenanigans, but there probably will be. Take care.

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