Local OpenClaw & Ollama in 27 minutes

[00:00] This tiny computer runs my Open Claw

[00:02] locally 24 hours a day. No cloud APIs,

[00:06] no token costs, and even if the internet

[00:09] goes down, it's still working. But

[00:10] getting this system working was way

[00:12] harder than I thought. I had to test

[00:14] different local models, configure

[00:16] networking, and even split the system on

[00:19] two machines. In this video, I'm going

[00:20] to show you how to run Local Claw

[00:22] locally, how to pick the right models

[00:24] for your computer, and a setup I'm using

[00:26] at home with a Jetson Nano and an old

[00:29] gaming laptop. Why should you run Open

[00:31] Claw locally? First of all, Open Claw is

[00:34] expensive to run. It eats up a lot of

[00:35] tokens and before you know it, you run

[00:38] out of credits or you're spending $100,

[00:40] $200 on your Open Claw. Secondly, if

[00:43] you're concerned with privacy, you don't

[00:44] want to send all your data to a public

[00:46] LLM.

[00:48] Everything stays within your network and

[00:50] nobody else can see your data. Most

[00:52] importantly, what I found was that my

[00:55] open claw keeps going down because

[00:57] either I'm out of credits or claw just

[01:00] went down the other day. The servers all

[01:02] went down and I couldn't call anymore.

[01:05] So when your cloud provider is not

[01:07] working for whatever reason, it's

[01:08] updated as model. You always have

[01:10] something running as long as your local

[01:12] server is running. So your open claw is

[01:14] available to you at all times. Another

[01:16] thing that caught me by surprise is that

[01:18] when I first started using Open Claw, I

[01:20] was able to use it with my OpenAI

[01:22] subscription, my Claude code

[01:24] subscription, and my Gemini

[01:26] subscription. And now Gemini and Claude

[01:28] bans users who use the pro plan with

[01:31] their open claw. So now you don't have

[01:33] to worry about the policies of all the

[01:36] different AI providers. You have full

[01:38] control. But I must also say that the

[01:41] concept of local AI is really good. But

[01:44] getting it running locally, it's really

[01:46] hard. Complex setup, you have to

[01:48] understand networking configurations.

[01:51] You have to have the hardware. You need

[01:53] to have a fast computer. Otherwise, it's

[01:55] just super slow. And it's taken me more

[01:58] time to fix and configure things than I

[02:00] would like. Running Open Claw locally

[02:03] gives you a lot of freedom, but you also

[02:05] become the system administrator. But

[02:07] what does running open claw locally

[02:09] mean? Actually, I think there's two

[02:11] components. Number one is where does

[02:13] your open claw run? And then open claw

[02:16] needs to call an AI model to process the

[02:20] request. And where is that AI model

[02:22] running? You can have a cloud setup

[02:24] where your open claw is hosted on a

[02:26] server somewhere on Amazon on hosting

[02:29] wherever it is and then that open claw

[02:32] calls open AAI or claude. That's a fully

[02:36] cloud setup or you can have a fully

[02:38] local setup where you have a machine and

[02:40] it runs open claw and on the machine

[02:43] have a local llm model running so it can

[02:46] provide the responses to your open claw

[02:48] or you can have a hybrid setup where you

[02:50] buy a Mac mini and it's running openclaw

[02:53] right in front of you in your house but

[02:55] it's calling open AI cloud gemini and

[02:58] that openclaw calls a cloud LLM. So,

[03:02] we're going to cover how to run OpenClaw

[03:05] locally on your own device and hosting

[03:08] the AI model locally within your house.

[03:11] We're going to go through two examples

[03:13] in this video. The first one is the

[03:14] beginner setup, which is everything on

[03:16] one machine. I'm going to show you how

[03:18] to install OAMA and then using OAMA,

[03:22] install Open Claw, and then run a local

[03:25] model. So, that's the beginner setup and

[03:27] then we're going to go [clears throat]

[03:27] into my current setup. I have a Jetson

[03:30] Nano which is like a tiny computer

[03:32] almost like a Raspberry Pi running open

[03:34] claw and then I have an old gaming

[03:37] laptop that's running Lama and serving

[03:39] the AI model. Why? Because number one, I

[03:42] don't want to run open claw on my

[03:44] MacBook because of security reasons. And

[03:46] number two, my MacBook is really slow.

[03:49] So when I talk to Open Claw, it takes a

[03:51] long time for it to respond. So, by

[03:53] running it on a old gaming laptop, I get

[03:57] much better performance. And I'm going

[03:59] to go into how to set that up. And I

[04:00] also want to show how you don't have to

[04:03] buy a super powerful computer like a Mac

[04:06] Mini to do this. You can do this on some

[04:10] computer you have lying around, an old

[04:12] gaming laptop, and put them together and

[04:14] make your own local Open Claw setup.

[04:17] Okay, enough talking. Now, let me show

[04:18] you step by step how to set up Open Claw

[04:21] on your own computer. The first thing

[04:23] we're going to do is go to alama.com and

[04:25] install Alama. There are two ways to do

[04:27] it. Either you can run this terminal

[04:29] command or you can download a lama. So

[04:32] you can press download and install it

[04:33] there. But the best way to use a lama is

[04:36] through the terminal. I'm going to copy

[04:37] this command and then just run the

[04:39] command. And then I can run a lama by

[04:41] typing in a lama. And now I can run a

[04:44] model launch cloud code launch codeex

[04:48] launch open claw. So, let's start with

[04:51] running a model. And then you're allowed

[04:54] to choose different models. And it's

[04:56] giving me recommendations based on my

[04:58] specs. The recommended list is not the

[05:00] best. So, you can choose, you know, GLM

[05:04] 4.7 Flash if you wish just to get

[05:06] started. I already have a model

[05:07] downloader, so I'm going to use that.

[05:09] And I'm going to show you how to pick

[05:11] the best one and update that later. And

[05:13] I'm going to give it a test. Hi. All

[05:14] right. And so, Alama is running. And now

[05:17] let's download Quen 3.5. So I go to a

[05:20] llama. I click on models. I click on

[05:23] Quen 3.5 and I'm going to select Quen

[05:26] 3.59B latest. Copy this. And then I type

[05:30] in OAMA run and then paste Quen 3.59B.

[05:35] And it's going to start downloading the

[05:37] model. And let me run that model. Okay.

[05:39] And it's done. So let's give it a test.

[05:40] Hi. And it works. The next thing we're

[05:43] going to do is a new thing that open

[05:46] claw has enabled which is you can now

[05:49] use lama to install open claw. To

[05:51] install open claw with lama all you need

[05:54] to do is copy this command. Alama launch

[05:57] open claw. Copy it. Then go to your

[05:59] terminal and paste that in. And then

[06:01] it's telling me to choose my model. I'm

[06:03] going to choose quen 3.59b. I understand

[06:05] the risk. Okay. So, it's finished

[06:07] installing and I just sent it a message

[06:10] saying, you know, I'm Keith and it

[06:11] responded, but it's taking a very long

[06:14] time. So, the problem with Quen 3.59B is

[06:18] that it's got reasoning and it thinks a

[06:20] lot before it does that. So, I'm going

[06:22] to tell Open Claw to set it to no think

[06:24] mode. It's taking too long to respond.

[06:27] Okay, now it's set it to no thinking

[06:30] mode. So, it should be faster. Now, now

[06:31] that you're set up, you also want to

[06:33] make sure your web interface is working.

[06:35] So when you first installed it, you

[06:37] should have an address like 127.0.0.1

[06:42] 18789. So let's go to that. We've opened

[06:45] our browser and then we're going to

[06:47] paste in the address. Now it's going to

[06:49] say gateway token missing. When you

[06:51] first install it, it should display a

[06:54] URL with a token equals something. And

[06:58] in my case I need to come to overview go

[07:01] to open gateway token and my gateway

[07:05] token is a llama. I press connect click

[07:08] refresh and once I click refresh

[07:11] I can see that on the top right the

[07:13] health is okay and I'm connected is all

[07:16] green. You can click on overview and see

[07:18] stat is okay. Then if I come to chat,

[07:21] you're going to see that the messages

[07:23] that I've been sending earlier are

[07:25] working. And I can also chat here. So

[07:28] let's give it a try. And it's responded

[07:31] to my high. It's working.

[07:33] Congratulations. You have local llm

[07:36] working with your open claw. I know that

[07:38] a lot of people already installed open

[07:40] claw. And if you're not using lama to

[07:42] install open claw, how do you add lama

[07:45] to your existing open claw? Well, if I

[07:48] come here in my web dashboard, I come to

[07:51] config and then I click on raw, you're

[07:55] going to see the configuration file. And

[07:57] in the configuration file, you can

[07:59] change your configuration file to

[08:02] models, providers, or llama. And then it

[08:05] sets it to quen 3.5 9b. But I have to be

[08:09] honest with you, I hate changing the

[08:11] config file. It's really hard. You keep

[08:14] making mistakes and it doesn't work. So,

[08:16] what's the best way to do it? I'm going

[08:17] to show you two ways to do it. Number

[08:19] one, we're going to use any vibe coding

[08:22] tool you have, OpenAI codeex or Claude

[08:26] Code or Gemini, whatever you have, you

[08:28] can use that to add to your model list.

[08:31] And then number two is to directly tell

[08:33] Open Claw to update your config file to

[08:36] include Lama in your model selection.

[08:40] So, let's go back to our terminal

[08:42] interface. And if I select slash

[08:46] open model picker, it's going to allow

[08:48] me to search. I'm going to type in a

[08:50] lama. Right now, it's only got a llama

[08:52] quen 3.5 9b. Now, let's say it's not

[08:56] even there. How do I add new models to

[08:58] that? So, I'm going to exit this. And

[09:01] you can use whatever you like. I'm going

[09:03] to use claude. And I'm going to bring up

[09:06] our llama. You can see I have GMA 3 4B

[09:10] which I downloaded a long time ago and

[09:12] it's not available in my model list. So

[09:14] let's add that. I'm using open claw menu

[09:17] and new addama

[09:20] 3 to my config get as a model. So what

[09:25] it's going to do is that it's going to

[09:27] search all the files on my computer find

[09:29] the config file and it's going to add to

[09:32] my config file so then they can find my

[09:33] model and it's done. So, it's found the

[09:36] config file and it's added GMA 4B

[09:39] automatically without me manually going

[09:41] in and making a mistake. Let's go open

[09:44] model picker. Okay. And you can see that

[09:47] now it's added GMA 4B. And before this,

[09:51] I realized I needed to restart my

[09:53] gateway for it to recognize. So, what

[09:55] you do is you need to type in open claw

[09:58] gateway restart and it will refresh and

[10:01] reload config. and then you'll be able

[10:03] to find the new model you just added. So

[10:06] that's the easiest way to add your lama

[10:09] models to an existing open claw when you

[10:12] haven't installed it using. And the

[10:15] other way is if you're already connected

[10:16] to a cloud platform and you can chat

[10:19] with open claw. What you can do is you

[10:21] can say can you add gamma 3 to my config

[10:24] file so I can choose as a model. Now

[10:27] that will work too but sometimes I

[10:29] realize it does funky things. The best

[10:31] way is to use Claude Codeex or Gemini,

[10:35] whatever AI coding agent you have to do

[10:37] it, but this also works too. So you can

[10:39] try that. And then the last way is to

[10:42] just go in into your config file and

[10:44] modify the configuration file manually.

[10:47] Now that we have open claw installed,

[10:48] this is the hardest part, choosing the

[10:50] right LLM for Lama. And the trick is you

[10:53] have to download another software

[10:55] because in Olama it doesn't give you

[10:57] much information. So download LM Studio,

[11:01] try out different model and then once

[11:03] you find the best model, run it in O

[11:05] Lama because Lama works better with Open

[11:08] Claw. It's got a smaller footprint and

[11:10] it just runs better. So go to

[11:12] lmstudio.ai and then download the app.

[11:15] And once you have it, click on the

[11:17] search icon, click on the discover icon,

[11:20] and you'll see all this great

[11:22] information. Compared to Lama, which

[11:25] only gives you the model name, LM Studio

[11:28] gives you a lot more. Most importantly,

[11:29] it gives you a best match. So, it

[11:31] recognizes your computer's

[11:32] specifications and then recommends the

[11:35] best one for you. What you have to pay

[11:37] attention to is two things. Number one,

[11:39] how big is the model? So, the larger the

[11:42] model size, the more powerful it is, but

[11:45] also the longer it takes to run. So,

[11:47] it's recommending a lot of smaller

[11:48] models for me, like 9B, 1.2 billion. But

[11:52] let's look at the most downloads. You

[11:54] have ones are 20 billion, 30 billion,

[11:57] and that's just too much for my computer

[11:59] to run. The second thing you should look

[12:01] for is you probably want to look for a

[12:04] model that is compatible with tool use

[12:06] because it means it's designed for agent

[12:09] use like Open Claw. So, pick models that

[12:12] have this symbol on it and then just

[12:14] click download. And after you've

[12:16] selected it, you need to give it a test

[12:17] run to test it speed and its

[12:19] performance. So, let's give it a try.

[12:21] I'm going to load a model and right now

[12:24] I have Quen 4B

[12:27] and I'm going to type test. Okay, so it

[12:30] came back and you can see that this took

[12:32] about 10 seconds and for me this is a

[12:36] little bit slow for me. I want something

[12:38] faster. So I'll test a different model.

[12:41] So keep testing to find the one where

[12:44] the speed is good for you and also

[12:47] you're happy with the results it's

[12:49] giving you. All right, I've been testing

[12:50] a lot of models and I made another video

[12:52] on setting up your own local AI, but

[12:55] here's what I found. It's basically a

[12:57] balance between speed and performance.

[13:00] So, with Open Claw, a lot of people say

[13:02] Kimmy K 2.5 is really good. It is really

[13:05] good. The results that it comes back

[13:07] with is really good, but my computer is

[13:10] not fast enough to run it. So, I get

[13:12] super slow response speeds. Then I tried

[13:14] using LFM2, which is super fast and

[13:18] lightweight. is one of the smallest and

[13:20] fastest models out there. But the

[13:22] results that it gives me are not very

[13:25] smart and I've switched to the current

[13:28] winner is Quen 3.59B. In terms of speed

[13:31] and performance is the best one so far

[13:33] on the market. But the key is you have

[13:36] to keep constantly updating. I was using

[13:38] Quen 3 6 months ago and now the model

[13:42] has improved so much. So, the key is to

[13:46] check for new models every month or two,

[13:49] play around with it in LM Studio, and

[13:51] then strike a balance between speed and

[13:54] quality. And I'm excited because the

[13:56] open- source models you can download are

[13:58] getting much better very quickly. Okay,

[14:00] now we're going to go into a more

[14:01] advanced setup. And this is my setup. I

[14:04] have a Jetson Nano, kind of like a

[14:06] Raspberry Pi, running my Open Claw. But

[14:09] although it is a cheap device, it's not

[14:12] powerful enough to run AI models on it.

[14:14] So I can only run open claw. So I use my

[14:17] old gaming laptop and on that I install

[14:20] llama and I run my AI model on that. Why

[14:23] don't I just run open claw on my gaming

[14:25] laptop as well? Well, because my gaming

[14:28] laptop is not designed to be run 24/7.

[14:31] And I do want it to run 24/7 because I

[14:34] want to message it at any time and I

[14:36] want it to run tasks overnight. is not

[14:39] designed for that. The Jetson Nano is.

[14:41] So, I have both computers running at

[14:42] home and this only turns on when I need

[14:45] to run AI models on it. And why the

[14:47] Jetson Nano? Well, I wouldn't recommend

[14:49] it. It's just something that I have

[14:51] lying around. The best option is

[14:53] actually a Raspberry Pi. So, you can get

[14:55] an $80, $150 option out there where you

[15:00] can buy a Raspberry Pi, buy a nice

[15:02] little case, and you can leave it on

[15:04] running with low electricity costs 24/7,

[15:07] no problem. or you can get a Mac Mini.

[15:09] More expensive, but you can even run

[15:11] your LL models allin-one. So, it depends

[15:14] on your budget. Enough talk. I'm going

[15:15] to show you my setup with a Jetson Nano

[15:18] running Open Claw and having my old

[15:20] gaming laptop as a Lama LLM server

[15:24] connecting to each other. Here's my

[15:26] Jetson Nano. I bought a little case and

[15:30] it even shows the temperature, the CPU

[15:32] usage, the RAM.

[15:35] That's my Jessen Nano running 24/7.

[15:37] Here's my old gaming laptop. It's

[15:40] running. It's on. And the screen isn't

[15:43] even on to conserve energy. And I'm

[15:46] plugged it in so that it can wake on

[15:49] LAN. I'm on my Windows machine right

[15:51] now, which will act as a server to my

[15:54] JSON Nano. And I've gone to the website.

[15:57] I've copied the command, which is this

[16:00] command. And now I'm installing it on my

[16:03] Windows machine. So, I typed in

[16:05] terminal, open terminal, and now it's

[16:08] installing OAMA. And so, I've installed

[16:10] it. And the next thing you need to do is

[16:12] to download a model. So, all you need to

[16:15] do is put in this command, Alama run,

[16:18] and then copy the model name and put it

[16:21] in. So, you can go to models. We're

[16:23] running Quen 3.59B. Copy that and then

[16:26] paste it in and then hit enter. And it

[16:30] will download the model and also run it

[16:32] at the same time. Okay. And it's done.

[16:35] So, let's give it test. And it's work.

[16:37] Okay. And it's working. So, I'm going to

[16:40] just exit this for a little sec. I'm

[16:43] going to exit this. Now, this is only

[16:46] running on my Windows, but in order for

[16:49] it to be a server, I need to allow other

[16:52] computers to access my Windows. So,

[16:55] first of all, I need to type in IP

[16:56] config. And you'll see that my IP

[16:59] address is 1 192.168.68.62.

[17:05] So I need to remember this address.

[17:06] Going to copy it. Then you have to run a

[17:08] command calledama serve. Now you'll see

[17:12] that right now it's serving on what's

[17:15] called localhost 11434,

[17:18] port 11434. And even though this is on,

[17:21] it doesn't mean that my other devices on

[17:24] the network can access it. So, I'll need

[17:27] to set the IP to the IP we just

[17:29] discovered earlier. So, I'm going to

[17:32] come out of here and I'm need to type in

[17:34] this command dollar sign envah host

[17:37] equals and then we're going to put in

[17:38] the address we did earlier. Okay, I put

[17:40] in the address and then now I type in

[17:44] serve and the host is now at this

[17:46] address. So from my other devices, my

[17:49] Justin Ano, I can now call this address

[17:52] and it should be able to communicate

[17:54] with it and use this as my server for my

[17:58] open call. But every time you turn off

[18:00] the computer and you restart it, your IP

[18:03] address may change. So I'm going to show

[18:06] you how I set a static IP on my router.

[18:09] And there's more. I'm gonna put this on

[18:12] wake on land. So then my computer can

[18:15] turn off when it's not being used and

[18:17] then only when it's being called by my

[18:19] open call, it'll wake up, turn on the

[18:21] power and then run the llama. Every time

[18:23] my gaming laptop shuts down and then

[18:26] powers on again, it will get a new IP

[18:28] address. And I don't want that because

[18:30] if my Justin Nano is calling it, we want

[18:32] a fixed address so it knows exactly

[18:34] where it is. So depending on your router

[18:36] settings, this is my router. I use TP

[18:41] link. What I do is I can come to more. I

[18:44] come to advanced.

[18:47] I go to address reservation.

[18:51] And basically I've reserved my device to

[18:55] always have a static IP on 192.168

[19:00] 6865.

[19:01] And so it's fixed and that's reserved

[19:04] for my Alama server running on my gaming

[19:08] laptop. So depending on your router

[19:10] settings, you need to set a static IP.

[19:12] And that's how you set it. Now I'm going

[19:13] to show you how to set some settings. So

[19:15] then you can have the computer turn off

[19:18] and then wake on land. So it only turns

[19:21] on when you actually need to use it. I'm

[19:24] going to go and restart my

[19:27] Windows.

[19:29] And then while it's restarting, just

[19:31] keep tapping the delete button to bring

[19:34] up the BIOS. Okay, now the BIOS is

[19:37] brought up. You want to go to advanced.

[19:40] You want to enable USB power and sleep

[19:43] in hibernation. You want to disable

[19:47] fast boot and then you want to enable

[19:49] wake on land and then you save and

[19:52] reset. Now depending on your machine,

[19:55] you might have a different BIOS. So

[19:57] check with AI what settings you need,

[20:00] but generally that's what you need to

[20:02] enable wake on land. One more thing,

[20:04] just make sure you use an Ethernet cable

[20:08] so you have a cable plugged in because

[20:12] it doesn't wake up if it's just on

[20:14] Wi-Fi. Now that I have Alama as a

[20:17] server, there's a quick little tip. By

[20:20] default, the context length might be too

[20:23] small. And so, you need to go and type

[20:26] in a command that says show and then

[20:28] quen 3.59B and it will show you your

[20:32] context length. So right now it's at

[20:34] 26,000 2,144.

[20:37] So I've increased it, but you might just

[20:40] see 4,000 here. And if that's the case,

[20:42] you need to increase it to a

[20:44] recommendation of at least 16,000. The

[20:46] way to set the context length is to

[20:49] enter run your model name dash context

[20:54] and then enter your number. Play around

[20:55] with the number. Obviously, the larger

[20:58] the context length, the better. But

[21:00] also, if you have a really long request,

[21:03] it might crash your computer. So, play

[21:05] around with this number. I've set mine

[21:07] to 26,000. At the minimum you should set

[21:10] it to is around 16,000. Another thing

[21:12] that I did was that once I've got the

[21:14] llama running, I don't want my gaming

[21:16] computer to be on all the time because

[21:19] it's not designed that way and it'll

[21:20] overheat really quickly. So, what I've

[21:23] done is I've gone into my system power

[21:26] and battery. I'm going to turn off my

[21:28] screen after 5 minutes of nothing going

[21:30] on. And then it will hibernate after 10

[21:32] minutes. And if it goes to sleep, that's

[21:34] fine. We're going to wake on land. So

[21:37] then when my other devices call this

[21:40] device, it will wake up. So how do you

[21:42] do that? Well, you have to come to your

[21:44] device manager and you have to look for

[21:46] your internet connection devices. So I

[21:50] have my Wi-Fi and I also have a LAN

[21:52] connection where you plug in a cable. So

[21:55] this is my Wi-Fi.

[21:57] So if I want to wake my Wi-Fi, I can

[21:59] come to power management and allow this

[22:02] device to wake the computer and press

[22:04] okay. Enable that. And the other one is

[22:08] my LAN cable and I do the same. And you

[22:10] can find this uh inside your network

[22:13] adapter. And once you do that, the last

[22:15] remaining thing you can do is to restart

[22:18] your computer and in your BIOS set wake

[22:22] on land. And one final thing is that you

[22:25] don't have to set a static IP. There's

[22:27] an even more advanced way to set this up

[22:29] and that's by using tail scale. By using

[22:31] tails scale, not only is it more secure,

[22:33] but you can also access this server from

[22:36] any device even when you are outside of

[22:39] your house. In my current setup,

[22:42] everything has to happen in the same

[22:43] network and you have to be on the same

[22:45] Wi-Fi to work. But let's say I'm out and

[22:48] about and I want to access my Olama

[22:50] server. I can do that using tail scale

[22:53] and tunnel in and it's even more secure,

[22:56] but that's for another video. So stay

[22:58] tuned for my other video. I'm back on my

[23:00] Mac. I have open claw installed. And

[23:02] then we have a lama set up on my old

[23:05] gaming laptop on a separate machine. So

[23:07] the first thing we need to do is to

[23:09] check if it can connect to it. You can

[23:11] do that by using a curl command in your

[23:13] terminal. So open your terminal, type in

[23:16] this command, curl http your IP address

[23:19] of your gaming machine/ ai/tags and it's

[23:23] returned that it's got a model 3.59b. So

[23:26] it's working. Okay, now that it's

[23:28] connected to it, let's see if it

[23:29] actually runs. So the next command is

[23:32] curl API generate d choose a model. Say

[23:37] my prompt is hello and I want to choose

[23:38] stream. So I'll include this prompt

[23:41] inside the description and you can see

[23:43] that it's coming back with some response

[23:45] and it's thinking. It's thinking. Let's

[23:47] check back in when it gives me a result.

[23:49] Okay. And it's done and it's giving me a

[23:51] response. So it's working. So now we're

[23:53] ready to go to open claw. So let's go to

[23:56] open claw tui. So now that my MacBook

[23:59] can connect to the server. There's three

[24:01] ways to change the configuration

[24:03] settings. So then open claw can use the

[24:06] model remotely. Number one, you can go

[24:09] into config file and paste this into it

[24:11] and set your provider as a llama, your

[24:14] IP address and the model. You can do

[24:16] that manually, but every time I've done

[24:18] that, it has not been successful. But if

[24:20] you really want to do it, all you need

[24:21] to do is go to finder, go to your home

[24:24] directory, and then go command, shift,

[24:27] and period. And you're going to see all

[24:29] the hidden files. And in there, I'm

[24:31] going to find open claw. So let's find

[24:33] open claw openclaw.json. Let's open it.

[24:36] And here I can input my I can copy this

[24:40] the model provider and paste it into

[24:43] here. But so far every time I've done

[24:45] it, it has not been successful. So I'm

[24:48] going to do in my opinion the best

[24:50] option which is to use vibe coding to do

[24:52] it. So in this case I'm going to use

[24:53] cloud code. You can use openai Gemini.

[24:56] I'm going to choose a folder and I'm

[24:59] going to go back into the same folder.

[25:01] I'm going to go back into open claw.

[25:03] Okay. And then I'm just going to say

[25:05] modify the open claw config file. So

[25:08] then it points to my remote or llama

[25:11] server. And then you plug in the

[25:14] details, your IP address and the model.

[25:17] And then it should automatically make

[25:19] those changes. Okay, it says it's done.

[25:22] And so I need to come back. And then the

[25:24] thing I need to do is open call gateway

[25:27] restart. So, I need to restart the

[25:28] gateway to activate the change and then

[25:32] let's test it out. And there we have it.

[25:34] It's returned a response. Hi there, I'm

[25:36] still awake. Let's start with the

[25:38] basics. So, it's working right now. And

[25:40] the third option is a chicken and egg

[25:42] problem. You can ask open claw to update

[25:45] the model similar to how I did it in

[25:47] cloud code. Just tell it that you have a

[25:50] new remote server. Give it the IP and

[25:52] the model and it will update it. But the

[25:55] chicken egg problem is that you need an

[25:56] AI LLM provider connected to do this. So

[25:59] either connect to your cloud service to

[26:01] do this or you can run a really small

[26:04] model on your local computer just so

[26:07] then you can get the configuration done

[26:08] so then openclaw can set its own config

[26:11] file. So those are the three options you

[26:12] can use to connect and change your model

[26:16] to point to a remote server. So that's

[26:18] how you run open claw locally. We

[26:20] started with the beginner setup where

[26:22] you're running openclaw on just one

[26:24] machine and then I showed you my current

[26:25] setup where a small computer runs open

[26:28] claw 24/7 and another machine runs AI

[26:32] model. But the bigger question is should

[26:34] you run open claw locally at all? If

[26:37] you're an absolute beginner honestly the

[26:39] best option is to run open claw on a

[26:42] virtual private server. So, you rent a

[26:44] server somewhere and then call a cloud

[26:46] model like OpenAI, Gemini, or Claude

[26:48] using their API. There are ton of

[26:50] one-click services out there for Open

[26:52] Claw and you'll spend much less time

[26:54] configuring things. But if privacy

[26:56] matters to you or you're worried about

[26:58] the API cost, then running Open Claw

[27:00] could be a very powerful option. Your

[27:02] data stays on your machine and your

[27:04] system keeps running even if the cloud

[27:06] goes down. And the number one

[27:08] requirement is that you need a computer

[27:10] that's fast enough to run a local LLM.

[27:13] In my case, I'm just using an old gaming

[27:15] laptop, and that works surprisingly

[27:17] well. The trade-off is that local setups

[27:20] require more work, networking, hardware,

[27:23] testing different local LLM models until

[27:25] you find the right balance between speed

[27:28] and quality. But once everything is

[27:30] running, you basically have your own AI

[27:32] infrastructure at home. And that's

[27:34] pretty sweet. Local AI gives you the

[27:36] control, but it also makes you the

[27:38] system administrator. If you've liked

[27:40] this video, please like and subscribe to

[27:42] my channel. If you want to learn more

[27:43] about AI, you can join my free AI

[27:45] community in the description. I look

[27:46] forward to seeing you