So What? Setting Up A Local Model for AI

So What? Marketing Analytics and Insights Live

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In this episode of So What? The Trust Insights weekly livestream, you’ll learn about setting up a local AI model. You’ll discover the essential components required for a local AI model setup, including the client, server, model selection, and database. Explore practical use cases for local models, especially for enhanced privacy, and learn how to avoid common pitfalls during setup. Discover how local AI models can be a game-changer, and get ready to unlock the potential of AI on your own machine!

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In this episode you’ll learn:

• The bare bones setup of a local AI model –
• How to narrow down the 1.25M available AI models –
• Maintaining your local model environment

Transcript:

What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

Katie Robbert: 00:35
Well, hey, everyone. Happy Thursday! Welcome to So What?, the Marketing Analytics and Insights live show. I am Katie, joined by Chris and John. How’s it going, guys?

Christopher Penn: 00:42
Happy New Year.

Katie Robbert: 00:44
Happy New Year.

Christopher Penn: 00:45
This is our first show of the new year.

Katie Robbert: 00:47
You took the words right out of my mouth! So today we are setting up a local model for AI. We’re coming hot right out of the gate, and before we get into doing that, what we’re going to do is the bare bones—because my understanding is that it could get pretty extensive and unwieldy—on the podcast earlier this week. If you want to subscribe, it’s at TrustInsights.ai/TIPodcast. That’s TrustInsights.ai/TIPodcast. Chris and I talk through what is a local model for AI and why you should care about it so you can get all of that information on the podcast. But as a quick recap, a local model is something that lives on a local machine. It is exactly what it sounds like.

Katie Robbert: 01:37
It is local to your machine—not hosted in the cloud or something like that—local to your machine. And you pick a model. There are what, as of today, over 1.25 million models to choose from. So, no big deal. I think there’s a big misunderstanding that it’s just the major models—like ChatGPT, Gemini, Claude, Llama, Meta, whatever—lots of models. And that’s just, to name a few. And by that, I mean just a few of 1.25 million. So what we want to do today is do the bare bones of setting up a local model for AI. So, Chris, where would you like to start?

Christopher Penn: 02:21
We should probably start with a very quick recap of what constitutes the local AI infrastructure, because I think it’s important to make sure that we’re all literally on the same page in terms of terminology. So there are four pieces to local AI. You need a client—some piece of software that is an interface. Now, when you use ChatGPT, the client is your web browser. And in a lot of cases, different systems have web browsers, but there are also apps that you can run on your desktop computer that are the client for local AI. Second, you need a server. You need something that can load a model, because a model is nothing more than a database.

Christopher Penn: 03:00
It’s a big old database of statistics, and you need to have something that serves it up in the same way that the internet has a web browser and a web server—like Apache or Microsoft Information Server, whatever. AI has a client and a server as well. So you need some kind of server. Third, you need a model. You need to pick a model out of the millions of variations there. And, by the way, there are 1.25 million models, but then there are 10–20 variants of each model based on size. And the fourth optional thing that might be a good thing to have would be your data in a format that’s compatible with a model of your choice. So those are the four pieces of local AI. You look puzzled.

Katie Robbert: 03:45
Katie, I’m curious as to why your data is optional.

Christopher Penn: 03:51
Your data is optional because if you’re doing things like—here’s a podcast transcript, summarize it and stuff like that—you don’t need to store that in one of these specialized databases. The “your data” section here, in this context, is for if you want to have, like, say, a database of all the Trust Insights podcasts ever, or all the newsletters ever—that will exceed the working memory capacity of most models. So you need a specialized database system to store it in so that the model can go and retrieve things from its library as you use it.

Katie Robbert: 04:23
So it’s data storage, not your data.

Christopher Penn: 04:27
Correct. It’s a data; it’s data. Specialized databases that are made for AI.

Katie Robbert: 04:32
Okay, yeah. I think as a point of clarification we might want to update that. Because when I read this as a non-technical person, I see it says “your data,” and I’m like, well, isn’t that how it works? Like, I put my data in, and it does something with it? So, yeah, I think “AI database” makes more sense because my initial thought is that’s why I was confused. I’m like, why is my data optional? Isn’t that what I’m putting in to do the thing?

Christopher Penn: 05:02
Exactly.

Katie Robbert: 05:03
The gas that makes the engine go.

Christopher Penn: 05:05
Exactly. The second thing we need to talk briefly about is when it comes to picking models; you can’t just pick one off the shelf. One of the things you have to know is that models come in different sizes that are typically measured. You will see terms like 70B or 120B or 7B. The “B” stands for billion. That means the number of parameters—basically how much data is in the model. The bigger that number, the more computer power you need to run it. So a 1 billion parameter model—like Microsoft Phi for 1 billion—that can run on an iPhone, right?

Christopher Penn: 05:42
That is a small enough model that your phone’s got or a Samsung Galaxy; it’ll run on a very—you know—this is such a small model. One of these—in fact, the Apple intelligence system is nothing more than a series of small models that Apple has crammed into this little hardware. On the far other end is Meta’s Llama 2, which has 670 billion parameters. And, on disk, it’s something like—I want to say—something like 2 terabytes. You need a server room to run that. You need a lot of computers chained together to run that particular model.

Katie Robbert: 06:25
So I’m going to be totally transparent with you, Chris. You just said a lot of things, and my brain went off elsewhere. Pretty much, yeah. And the reason why I bring that up is not to say, oh, Chris, what you’re saying is so uninteresting. It is incredibly interesting and important. But this is why you need someone who knows what they’re doing to guide you through setting up a local model. You sort of lost me at what “B” stands for. And I say that not because I want to be like, oh, Chris, what you’re saying is so dry. No, it’s incredibly important. And if you are someone who’s just kind of dabbling, make sure you are watching some of Chris’s stuff, making sure you know what you’re doing.

Katie Robbert: 07:14
Because I think—correct me if I’m wrong, Chris—you could end up costing yourself a heck of a lot of money if you do it wrong.

Christopher Penn: 07:24
Yeah, I mean, you generally can’t go wrong buying more computer.

Katie Robbert: 07:29
Well, if you can’t afford it, if you don’t need it, it’s time, it’s effort, it’s all the things. All right, so there are four pieces to the local AI architecture.

Christopher Penn: 07:42
Yes.

Katie Robbert: 07:42
We are going to do a bare bones setup—just a very basic setup—to demonstrate what it looks like to have a local model. And again, if you want to get all the reasons why having a local model for AI set up, check out the Trust Insights podcast at TrustInsights.ai/TIPodcast. Chris and I go through all of the “why” of local AI.

Christopher Penn: 08:05
Right. So there are two pieces of software that, for the bare bones beginner, I would recommend: either AnythingLLM or LM Studio. Both are good. If you are a Mac user on an M-series Mac, use LM Studio, and we’ll talk about that in a little bit more. But those are the two pieces of software that I think would be best for that bare bones setup because they’re kind of self-contained. They can act as a client; they can act as a server; and you can load the models from within their interfaces. So you don’t have to be gathering all the pieces. If you want to get fancy, this is what your setup could eventually look like. You would have all sorts of bits and bobs and pieces and routers and all this stuff.

Christopher Penn: 08:49
But, for today, we’re going to focus on what is—what can be—the bare minimum that we can get away with. So I’m going to go ahead; I’m going to start with AnythingLLM. I’m going to use that one to start, mostly because I have not set it up on this computer yet. So it’s a good opportunity for me to set it up. So you go to Google for AnythingLLM; download the version for your operating system; available for Windows, Linux, and Mac.

Katie Robbert: 09:11
I feel like I don’t know. John, what do you think? It’s kind of a genius name because at first I was like, Chris is putting “anything” in LLM, meaning like, oh, anything you can find that’s LLM. But that’s actually the name of the software. So when people search for, I don’t know, AnythingLLM, it seems like such a genius marketing ploy. What do you think?

John Wall: 09:35
Yeah, they’ve definitely got their naming down. And you can see the interface here is very simple. There’s one button for dummies.

Katie Robbert: 09:40
So yeah, I love it already.

Christopher Penn: 09:44
Let’s push the button. All right, so this is where things go off the rails immediately. It asks you what you want to use. So the top choice is AnythingLLM, which will install its own AI engine on your computer. You can also use the big name brands. If you wanted to just have a desktop interface to the big name brands, you can use all these different platforms. I use one called Cobalt, which is a personal preference and has a very high technical bar. I would not recommend it for a beginner. The beginner’s choice really is this one up top, the very first one. So we’d start there, and then you see a variety of different models that you can choose from. I’m not going to pick one right now.

Christopher Penn: 10:31
Let’s see if it will let me do that. So it’s going to say, here’s how we’re going to do things, you guys. It’s going to install the Llama engine—the server for you—it’s going to create its own embedder—which is a way of taking documents apart and converting them into AI formats—and it’s going to install a local database, LanceDB, which takes the documents that you load—like PDFs—and stores them in AI format locally on your computer. Let’s see what happens next. Skip survey, my workspace, name my workspace. I’ll be super creative here. Let’s see what’s going on. It is doing a nice little intro here. And did you do the thing? I don’t see a whole lot of things happening.

Katie Robbert: 11:26
What should you expect to see? I guess that’s the question. So, if you’re me, for example, I’m looking for the “I don’t know what I’m doing” button, or “this is what you should expect to see” button. So what should someone expect to see?

Christopher Penn: 11:43
I’m assuming we just go on, touch on our first workspace—the one I called “my workspace.” Okay, let’s see if it actually did everything. And that’s that. Okay, so it says, hey, dummy, you didn’t put a model in, so we should choose our model. Now, I’m curious. It did not install the Llama engine, which I’m very curious about. That seems like it should have done that for you, and I’m not.

Katie Robbert: 12:20
Is it because—so basically you’re saying—so you clicked on AnythingLLM, you said “get started,” and you chose the server. You’re saying that theoretically it should, by default, if you pick the server, it should do the corresponding model?

Christopher Penn: 12:37
Yes. Oh, see, I—that there it went. I didn’t pick a model on that first setup screen. So it did nothing. I picked a model, and it says, hey, I’m going to do this in the background for you.

Katie Robbert: 12:47
Which I guess, in some ways, is kind of nice that it doesn’t automatically just install something; like it forces you to really think through and pick.

Christopher Penn: 12:55
Yeah. But if you don’t know what you’re doing—like, which of these models is the right one for me?—because these are actually what they’re showing on screen; these are not the ones I would pick.

Katie Robbert: 13:05
So it’s going to be very meta—not Meta the company, but meta the concept. Can you go into generative AI and say, what model is the model for me?

Christopher Penn: 13:16
Maybe. See, here’s the—here’s. There are two things at work here. One, the choice of model is going to depend on what you want to do. When you talk about a big foundation model—like GPT-4 or Google Gemini or whatever—those models are so big and so resource-intensive; they can pretty much do anything you throw at them and say, like, you know, I want to write some code or some poetry or compose a song or make an image of a dog on a skateboard wearing a tutu—and all because they have such massive infrastructure; they can do all those things without a lot of specialization. When you start doing it with local AI, you start saying, well, some models are good at some things and bad at other things. And this is where you start having to get into, well, how do I know?

Christopher Penn: 14:06
And the answer is you have to test a lot of things or hang out in Discord servers. I hang out in a lot of the Discord servers for these places, and a lot of people have sort of their hot list of, like, here’s what we’ve tested and know these models are each good at. So, for example, if I go back to my slides here, I have listed a bunch of models here. Llama 3.3 is sort of a really good general-purpose one. I need to add to this list here because I just tested this out: Microsoft Phi. Gemini 2 is Google’s model. It’s very—it’s good at following instructions—not particularly very creative, but it’s good at if you want to do things like summarization or extracting entities.

Christopher Penn: 14:52
It’s very good that Mistral’s Nemo is a good general-purpose model with some coding capabilities. Cydonia Magnum is an outstanding writer. If you want to do creative writing, it is great at that—it’s dumb as a bag of hammers and other things. DeepSeq is a good coding model; so is Quinn Coder. They’re good at coding; they can’t write; they’re terrible at writing. And then Phi is a good calculation model that can do some basic math.

Katie Robbert: 15:18
So two things to that, as we’re getting set up: one, it sounds like it’s a great opportunity to do some user stories. If you’re not familiar, a user story is a simple three-part sentence: As a persona, I want to do that. So ahead of installing a lot of heavy machinery on your laptop and making it suffer for no reason, definitely write some user stories. Because, to Chris’s point, not every model is good at everything. So it’s good to go in to have a clear understanding of why you’re using a local model, what kinds of things you want to be able to do, and to that at a high level. At the end of last year, we actually did a bake-off of the major models.

Katie Robbert: 16:03
So you know, OpenAI, Gemini, Anthropic, Claude, and so on, for the kinds of common use cases that people use generative AI for. So you can catch that at TrustInsights.ai/YouTube. Go to the So What? playlist, and you’ll be able to find that past episode. But, Chris, to your point, you should kind of know what you want to do with it going in. It’s not really—at least what I’m hearing—installing a local model just for the sake of installing it is probably not the best use of time.

Christopher Penn: 16:37
That’s right. The other thing I don’t love about this setup here is that it doesn’t take into account the system resources that you’re going to need because it will not necessarily work well on all computers. One of the things that you should have a general idea of is how much memory—working memory—your computer has. And it comes in two flavors. There’s regular RAM, which is your computer’s regular memory, and then there’s what’s called VRAM, which is your graphics card memory. Most office laptops—the low-cost Lenovo crap pads, whatever—those things have 8 or 16 gigabytes of RAM and no VRAM at all because they don’t have a graphics card. You don’t need a graphics card to browse the web and send emails.

Christopher Penn: 17:23
Your average gaming laptop is going to have between 8 and 16 gigabytes of VRAM, plus 16–32 gigabytes of RAM. In the Windows world, if you’re on a Mac, all the memory is together, so it shares everything. One of the things that you have to have a sense of—you can see it on screen here a little bit—is that model size there, 7.9 gigabytes. That’s how much memory it’s going to consume when you load it. So if you have a laptop that has 8 gigabytes of RAM, this model is not going to work for you. It’s going to—your computer’s just going to grind to a halt. On the other hand, if you have like the latest and greatest MacBook, you can leave something like this running the entire time.

Christopher Penn: 18:07
And you can do other things—play games, watch YouTube, and all that stuff—and you won’t even notice a performance hit. So that’s a consideration. You should know what you have—on your laptop—to know even what models you’re eligible for.

Katie Robbert: 18:22
What do you think, John? Would this blow up your machine at this current stage?

John Wall: 18:27
I would have no problem getting my laptop to burst into flames with some of these models. One thing I did want to ask, though: in that model selection process, it would seem like you would want to be using hosted models for all your testing. But are there any cases where the local model behaves so much differently than the hosted version that you can’t do the testing there that you’d have to download locally?

Christopher Penn: 18:54
So hosted models—like Google Gemini or whatever—are so much bigger than local models. So Gemini is—I believe it is—a mixture of expert models. I want to say it’s—it’s like 10 trillion parameters. So you would never be able to even load it on a laptop. There’s no laptop in the world that would even be able to run that. For these use cases, going back to what Katie was saying about the user stories, that’s really where you’re going to make the determination about which model you need and do you have the resources to run it? So let’s say you wanted to run Quoc V. Le’s model—which is the sort of the best in class coding model right now for non-supercomputers—that’s a 32 billion parameter model. It takes up between 20 and 30 gigabytes of memory.

Christopher Penn: 19:44
If you don’t have that, you can’t run it. And you would have to use something like an online service. Where you would want to use something like one of these local models is if you’re working with stuff that you just cannot put in the hands of a third party. So maybe you are a government employee of a three-letter government agency, and you know you’re getting data back from some of your colleagues in Damascus. You can’t put that in Google Gemini. There’s no way you could do that. Maybe you work in a hospital, and you’re dealing with medical imaging. There are models like Llama 3.2 Vision that can look at image scans now with the accuracy—it won’t be as good as Google Gemini—but you cannot put other people’s healthcare data into a third-party model without a gazillion and a half certifications.

John Wall: 20:35
There’s a lot of dials as far as how good do you want it to be versus how much power you have in your own hands.

Christopher Penn: 20:42
Exactly. It’s a linear relationship. The bigger the model, the more capable it is, but the more resources it’s going to take to run it. This finished loading. If we go into our workspace, let’s start a new thread immediately. This looks a lot like a ChatGPT: say, “Hey, today I want to summarize a podcast transcript. Can you do that?” It’s like, “Hey, I noticed you got something in there already.” I’m like, no, I’ll just say, it queues the transcript. Here’s the transcript; summarize it. Now, I will say these are terrible prompts; these are appallingly bad prompts. You would want to spend time.

Katie Robbert: 21:39
I would say they are normal, common prompts.

Christopher Penn: 21:45
You are also correct. Both things can be true.

Katie Robbert: 21:49
This is true. Yes.

Christopher Penn: 21:51
It says the speakers are discussing changes in AI models and the impact on business, and so on. It took my document, it converted it, and then it can provide a summary of it. Let’s see. What about the—what about this tutorial? Summarize it. So again, what it’s doing behind the scenes, it did not pick up that I gave it a document. It’s like, I don’t know what to do with this. Instead, it’s looking at the podcast transcript. So it did not see that attachment get transcribed properly. But this is what, essentially, is the—you now have a local AI that’s running Llama 3.2—which is the previous version of Llama—and it’s running, I believe, the 11 billion parameter model, which is a small model, which means it’s not super bright.

Christopher Penn: 22:50
You have to provide more data and more context to it.

Katie Robbert: 22:56
I’m going to be honest, because we are—what—20-23 minutes into this live stream, and you’ve already done the thing. I’m a little underwhelmed because—and I say that because I feel like the way in which we talk about the complexity of artificial intelligence, setting up local models—you have the four parts: you have the client; you have the database; and you have the model. I really expected something more complex and something more time-consuming. And yet, here we are, 20–25 minutes in; you literally just pushed a button, John and I bantered for a couple of minutes, and boom, magic. It’s done; like that. It felt way too easy.

Christopher Penn: 23:44
It is because we have—this is the bare bones; this is the bare minimum to get something running. It’s not good and it’s not tuned, and we’ve done none of the actual configuration. If you want to get behind the scenes a little bit here—let’s make that go away because that’s not helpful—behind the scenes, you have a ton of stuff that you probably should spend some time configuring in addition to choosing the model. There’s the database; what embedder you want to use; how you’re going to split the text so that it understands it; how to process voice and speech; what kinds of transcriptions are built in; and then your general settings. Who can use the model that you’re running? What kind of workspace-based things? What kind of tools do you want to have available?

Christopher Penn: 24:37
And then you start getting into all the customization for actually making this thing run. This is where you get into a lot of that complexity—making the dials work. And this is again—AnythingLLM is my choice for the bare-bones beginner because it takes a lot of the complexity and tries to hide it. However, in doing so, it also makes it very difficult for you to get top performance out of it. So the tool that I use for local models—again—which is slightly more complex—is one called LM Studio—also free, also cross-platform. LM Studio has a very similar setup.

Christopher Penn: 25:23
You turn it on; if there are no models, you have to go and download some models; you have to find them; but this allows you to choose from all the many different models out there, and they have their staff picks—which is a nice way of saying, like, hey, these are the ones that seem to work best for most situations—and, critically, for Mac users. This piece of software can speak the Mac’s native AI language—a language called mlx—so you can find models that are tuned specifically for Macs that run about 40% faster than anything the same model in AnythingLLM.

Katie Robbert: 26:04
So I feel like again, it’s sort of a good opportunity to plug user stories, or even in this case, the 5Ps, because this local model on your machine, at the end of the day, is a piece of software, and it’s no different from anything else that’s out of the box. So think about your CRM software, your email marketing software, your web analytics software. You can install all of that right out of the box. You can literally just log into it, and boom, put in your website; there it is; it’s tracking something. But until you actually tune the settings to what it is that you want to be doing, it’s not really going to do much for you.

Katie Robbert: 26:44
So go through the five Ps: purpose, people, process, platform, performance; do your user stories to understand what it is you want to get out of the thing. So that then, by the time you’re setting it up and you know, installing, you’ve chosen the model; you know which one is going to work for you because you know what it is that you want to accomplish from it; you’re going to get what you want out of it. Because, Chris, to your point, you installed AnythingLLM. Yeah, anybody can do that. I can do that now. I can hit the button and do the thing, but I’m going to get really crappy results. And what I—so I can already envision my vicious cycle of, well, this model didn’t work; let me do another one. Well, this one didn’t work.

Katie Robbert: 27:24
Let me do a different one, and then I completely pull a John Wall, and my laptop is up in flames.

Christopher Penn: 27:31
Yes. And this is where you could start to get into the more technical stuff where, if you want to get these models to work well for your environment and your capabilities, you need to start understanding what some of the dials are—to just install the software and run it. Yeah, exactly. It’s pretty sure you’re going to get—you’re going to get average performance out of it. In LM Studio, for example, I’m going to go ahead and pick the model. I’m going to pick Microsoft’s Phi, and it starts by saying, how much memory do you want it to have? It can support up to—and, by the way, that’s lying—it can support up to 128,000 tokens. How long do you want your working memory to be, so it out of the box?

Christopher Penn: 28:16
And this is what AnythingLLM says; it just does for you behind the scenes. It says I’m going to give it a working memory of 4,000 tokens—which is about 3,000 words. So if you’re going to work on a big document, that’s not sufficient. You’re going to run out of memory real fast. However, the bigger you make this number, the more memory it’s going to consume. So it’s again, a good idea to know your system—to say, well, I want to use 2 million tokens—just like Google Gemini. Yeah. Okay. Your computer is going to blue screen to death since it can’t do that. Every multiple of two in here consumes two gigabytes of memory. So if I say I want the context window 4096, that’s about 4 gigabytes of memory.

Christopher Penn: 29:02
If I say I want to be 32,768 tokens, it’s going to say that’s a lot. Are you sure you want to do that? I can, because this Mac has a lot of memory, but it’s going to consume a lot of memory. So if I were to open up my system monitor and say, okay, I’m loading this model for you now, and oh, by the way, it’s eaten up 32 gigabytes of RAM, so you no longer can use that. You have to—it’s now being used by this model. The second thing is, along the side panel here, you can start doing things like system instructions and system prompts and changing how the model responds.

Christopher Penn: 29:40
So, for example, when you use the Trust Insights Pair Framework or the Trust Insights Repel Framework, at the end, we will say things like, hey, turn this into system instructions to use for the next time. This is where you put them. You can copy and paste them in here, and then you can have this particular chat be that translator or that blog writer, blog ideation, or whatever the thing is you want it to be. Then you get into the blood and guts of models, and this is stuff that when you’re using ChatGPT, you never see this stuff. You have—you have no control over it. When you’re using, like, Google AI Studio or OpenAI’s developer platform, these are the settings that you are expected to know if you are developing.

Christopher Penn: 30:28
With AI, there are things like temperature—how random, how wildly creative can the model be? How creative is it allowed to be? If you set it to zero, it’s going to act as dumb as a bag of hammers, but it will never hallucinate because it can only—it will only parrot back exactly what you typed in. If you set it to five, it will be like Keith Richards, and you will get nothing coherent back. Different tasks require different levels of creativity. Out of the box, it’s going to come out at 0.8. You can mostly range between 0.5 and 1; you are going to get decent results depending on the task you’re doing. Katie, you look flummoxed.

Katie Robbert: 31:12
I’m not flummoxed; I’m just—it sort of goes back to—my brain is always like, well, if you had done your requirements up front, you wouldn’t need to be playing around with all of these, trying to get it right, get it wrong, you know. But I’m also wondering, like, for the average user, you know, there are a lot of settings here that they have to get educated on. For example, even just showing like the amount of memory, someone who maybe isn’t as familiar with what that means might be like, well, I want the most, and not realize that they’ve just hosed their entire machine, and now it’s just a really expensive paperweight, or the temperature. You know, you’re talking about it in terms of creativity.

Katie Robbert: 32:03
I’m like, oh, well, I want it to be the most creative as possible because I’m using this for my writing, for example. But that’s not exactly what that means. And I feel like those are the kinds of things that I don’t want to get too far into the weeds in this particular live stream. But it occurs to me that there’s so much to learn before you even start installing things. But how do you learn it without installing it and playing around with it? It’s sort of like the old chicken-and-egg thing.

Christopher Penn: 32:35
Exactly. Exactly. And that’s 100% right. So, out of the box, these tools come configured with the most common settings. So if you installed it and you just never touch the settings, it would give you okay results. But knowing what is even available will help dramatically improve the results. So if you are writing, for example, there’s something like a MinP sampler. A MinP sampler will essentially cut off its ability to just endlessly repeat itself. So when you—for example—when you’re writing fiction, if you don’t have that setting turned on, you’re going to have a bad time because it’s going to say, like, and Katie lived happily ever after over and over again. Because that’s what—that’s what it thinks it should be doing when you’re prompting it to write, say, a fiction story.

Christopher Penn: 33:24
So let’s go ahead and do the exact same exercise. We’re going to take our transcript, pop it in here, and say, please summarize this transcript. And so it’s going to get started on this process; it’s going to load the data; it’s going to process the data; and then it’s going to hand it off to the model itself to do the summarization. When you do this in ChatGPT or the tool of your choice, all this happens behind the scenes. And one of the things that’s—I think—so powerful about local AI and local models is it really exposes just how complex these systems are and makes you understand them as not being these magical things, but as a series of components that all have to work together. So, out of the box with Microsoft’s Phi, in—

Christopher Penn: 34:23
in this environment, says, hey, this is a podcast on local AI, which is in fact correct. It did not hallucinate that. And it talks about the benefits of local AI, the terminology, the practical examples, and the conclusion that is, of course, Monday’s podcast. Now, let’s continue on. Who is the CEO of Trust Insights?

Katie Robbert: 34:46
Man, these models—they get this one wrong a lot.

Christopher Penn: 34:53
It says I don’t know. So Microsoft’s model saying, “I have no idea.” When we did this the other day on the podcast, we were using Mistral—the French model maker—and they said the CEO is me. So it hallucinated that; but it just said, “I don’t know.” But you also see its knowledge base starts at October and ends in October 2023.

Katie Robbert: 35:16
Sure.

Christopher Penn: 35:17
But yeah, you’ve been the CEO since 2017. But it just isn’t going to say.

Katie Robbert: 35:20
That’s a pretty poor excuse.

Christopher Penn: 35:22
Yes, it just doesn’t have that information in it. So this is part of one of—

John Wall: 35:27
Those things too where if you had chosen a larger model. Right. The odds of it having this included in the data set would be better.

Christopher Penn: 35:34
Yes, exactly. So as you add more—as the models get bigger—they add more data; they have more knowledge. So Llama 3.3 does know the answer to this and it does get it correct. However, you need a really big gaming laptop to run that model because it’s a 70 billion parameter model.

John Wall: 35:53
It’s funny. I get a real Photoshop 1.0 feeling of this. You know, it’s like there are a lot of knobs and dials you have to turn, and you have to be really careful about if your machine can take it or not. Otherwise, you’re going to spend a lot of time rebooting and, you know, trying to get yourself out of holes you’ve dug yourself into.

Christopher Penn: 36:10
Yep. But this is the bare bones. You have an all-in-one app, and there are many of them. These two are probably the two that I think are the most user-friendly and offer the most general functionality that will do what you want them to do. If you’re a Mac user, it’s strongly suggested to use LM Studio with the MLX family of models because it just works better. If you’re anybody else, run AnythingLLM. And if you want to get slightly more advanced, you can separate out the front end from the back end. You could choose—like I use the Cobalt back end a lot of the time for my stuff—because I know exactly what models I want to use, when I want to use them, how I want to use them. For the non-technical user, you don’t need to do that.

Katie Robbert: 37:01
So I know we talked about on the podcast—we talked about the reasons—so privacy, sustainability, consistency, cost-effectiveness, business continuity. If that’s not one of the reasons someone is doing this, should they still install a local model? What if they’re just playing around?

Christopher Penn: 37:25
I think playing around and education and professional development is one of the best reasons to use these tools. Because when you use something like ChatGPT or Claude or Gemini within the web interfaces, these companies that make these things are adding a lot of extra stuff behind the scenes to try and mask the weaknesses of generative AI models because they are limited in what they are capable of doing. They have no memory whatsoever. They’re called stateless tools. Every time you prompt one of these things, it’s like a brand new slate. It’s like an intern with perpetual amnesia—never remembers anything except in that moment. These companies have added all these extras in the web interface to try and patch some of the weaknesses, but it’s not inherent to the technology itself.

Christopher Penn: 38:14
When you start messing around with local models, you learn real fast what they can and cannot do at a very granular level. And especially if you want to start building, like, apps or things that have AI embedded in it, you really need to know this stuff because you will be calling APIs and not a web interface. We were having a conversation with a client yesterday who was saying, hey, what I get out of ChatGPT on the web is not what’s coming back from the API. And we looked at it and said, well, yeah, because the web interface contains a lot of weird little add-ons that they don’t tell you about to try and fix what are fundamental shortcomings in the technology itself. So installing and running local AI is a fantastic way to learn this stuff.

Katie Robbert: 39:03
What do you think, John? Are you going to start setting up some local AI?

John Wall: 39:07
You know, this is one of those projects where, yeah, if I came up with the right use case, I’d do it, and it would just be endless frustration.

Katie Robbert: 39:17
Well, you could do some user stories, work it out ahead of time and try to see—you know, for example, John—like you do a lot of our business development; you have the Marketing Over Coffee podcast. I can definitely see using a local model for the business development work so that you’re not putting any PII out into the public models, or in some ways giving away your secret sauce in terms of how you do business development. So I can definitely see that as a use case, and then maybe—who knows—it could just make it more efficient for the work that you do with the podcast. I mean, those are things that I think could definitely be explored.

Katie Robbert: 40:02
But it just comes down to—you know, as Chris says—it’s professional development; you know, making and carving out the time to do it. And I know that—we’re all so overwhelmed and overloaded with everything—that making the time to do professional development is probably the hardest part of all of this.

Christopher Penn: 40:21
One example with Marketing Over Coffee, in particular, would be things like transcription, right? You have 800 episodes, and you’ve got a transcription vendor that charges you by the number of minutes you’re allowed to use every month. It’s going to take a while to get caught up. If you have a local AI model that does transcription, you can—it’ll take time, and your computer’s fans will go for a while—but you can get that all done for the cost of electricity on your computer as opposed to loading it to someone else’s data center and things. So there’s—if you’ve got a lot of backlog data—when we did the Halloween candy episode of the live stream, we used—I believe—we used Gemini 2 for that.

Christopher Penn: 40:58
And it was just looking, reading through articles and pulling out the named entities of all the Halloween candy set that we saw. That was 7,000 articles. That would have been a $200 bill just for that episode for compute. Instead, it was a day and a half of my computer’s fans running. But it got it done. So even cases like that where you just need to process a lot of data, there are good reasons to do that. I want to take a couple of minutes towards the end here to talk about—like what stuff? If you were going to go buy things, should you buy? Because this week at CES, of course, Nvidia announced their desktop supercomputer AI workstation called Digits, which for only $3,000 will get you an AI computer that does pretty much only AI.

Christopher Penn: 41:41
This is a good idea for a small to mid-sized business. If you wanted to run a local AI model as a server internally at your company, you could buy one or two or three of these boxes, chain them together, and with a lot of setup—a lot of very technical knob fiddling—you could have essentially a private version of these models that anyone in your company could open up a web browser and access that. So that’s a really nice option for small to mid-sized businesses that want to have very private AI if you’re doing something you absolutely cannot hand off.

Christopher Penn: 42:13
So I could see huge applications for this, say in medical practices, or you’d have a box inside the doctor’s office and that contains a connection to the patient database that has a language model, and your healthcare data never leaves their network.

Katie Robbert: 42:28
That makes a lot of sense. And that’s a very specific use case. Like, you know, you have all the information there. You don’t need to be going—you’re like—if you’re googling for the answers, you need to find a different doctor’s office. You know, you should have all of the medical text and all of that in a database pre-loaded. Like, the knowledge base should be static, for lack of a better term. You know, because this is what a broken bone looks like; we’re not going to innovate for what a new version of a broken bone looks like. You know, the treatments might change, but overall, like the medical information is the medical information. So I think that’s a very good use case. Where I start to get a little squirrely about it—

Katie Robbert: 43:09
is all of us marketers thinking that we need this—you know—for whatever the heck it is that we’re doing—when really it comes down to—well, we’re just writing content.

Christopher Penn: 43:19
Yeah. For the average marketer, what I would say is get yourself the nicest laptop that your company will let you get. And get—if you’re getting a Mac—get it with as much memory as your company is willing to spend money on. This is the MacBook Pro. You can see that 128 gigabytes of unified memory is a thousand dollars extra on top of it; makes it like a $6,000 laptop. Now the nice thing about that is it’s not just for AI. Like you can use it for other things like video editing and stuff like that. If you’re in the Windows universe—no—ASUS just announced the—what is it called?—the ProArt StudioBook 16, which is a mouthful of a name, but it has the largest amount of video memory of any laptop—24 gigabytes.

Christopher Penn: 44:05
So it can run a lot of models very capably as well. Those would be again, if—here’s—here’s the easiest rule of thumb for local AI: if you can play like a AAA title video game—like Call of Duty or World of Warcraft—on your laptop at full speed, full resolution, you know, all the knobs and dials turned all the way to the top, and your laptop does not burst into flames, your computer is really ready to run local AI.

Katie Robbert: 44:32
You just brought me back to my Windows Lenovo days when I worked at an office, and opening a Word document would make the fans just churn and churn and churn.

Christopher Penn: 44:42
That is not a candidate for AI.

Katie Robbert: 44:45
Not a candidate. That is—I mean—that’s when you go make a couple more cups of coffee and go talk to your neighbor.

Christopher Penn: 44:52
Exactly. But again, going—you know—sort of as we wind down here, having these tools available locally is a good idea. One of the other ones I think—and we talked about in the podcast—but business continuity is so important. We live in very uncertain times. You know, like, oh, all of Hollywood’s on fire, and there’s no guarantee that a data center and things will be available. If you have—if generative AI is part and parcel of your secret sauce—now, as a marketer, as a business, then you owe it to yourself to have these backup options. Final thoughts, John? Just start playing around with it.

John Wall: 45:37
But we give up all liability if your machine lights on fire or you get yourself painted into a corner. That’s not our problem.

Katie Robbert: 45:45
Agreed.

Christopher Penn: 45:47
I will say if you want to talk about—like—which model should I use? We get this question a lot in our analysts from our select group—pop over there and say, here’s what I’ve got, here’s what I want to do. What are some options? And we’ll be more than happy to advise people in the Slack of, like, hey, if you got this machine, here’s what you should do, or say, you know what? That machine’s going to burst into flames; just use ChatGPT.

Katie Robbert: 46:12
I think that makes sense.

Christopher Penn: 46:15
All right, well, that’s going to do it for this week’s show. Thanks everyone for tuning in. Hope that your new year’s off to a great start, and we will talk to you on the next one. Thanks for watching today. Be sure to subscribe to our show wherever you’re watching it. For more resources and to learn more, check out the Trust Insights podcast at TrustInsights.ai/TIPodcast and a weekly email newsletter at TrustInsights.ai/newsletter. Got questions about what you saw in today’s episode? Join our free Analytics for Marketers Slack group at TrustInsights.ai/analyticsformarketers. See you next time!

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Trust Insights (trustinsights.ai) is one of the world's leading management consulting firms in artificial intelligence/AI, especially in the use of generative AI and AI in marketing. Trust Insights provides custom AI consultation, training, education, implementation, and deployment of classical regression AI, classification AI, and generative AI, especially large language models such as ChatGPT's GPT-4-omni, Google Gemini, and Anthropic Claude. Trust Insights provides analytics consulting, data science consulting, and AI consulting.