Are you paying every month for AI access tied
to those big cloud services? And maybe worrying
where your private thoughts or your company -sensitive
docs are actually going, we kind of assume powerful
AI needs, like a $20 ,000 computer. But that
whole idea, it's pretty much outdated now. Welcome
to the Deep Dive. Our mission today is to take
this stack of research we've got here, focusing
on tools like Alama and LM Studio, and turn it
into something really clear. actionable. We want
to make local private AI something you can do
right now on a laptop you probably already have.
We'll cover the privacy wins, the cost savings,
and then the how breaking down things like quantization
and picking the right AI model for your machine.
Right. This is really all about taking back control
over your own compute. These terms like parameters
and quantization, they sound complicated. Have
you been intimidating? We're going to pull back
the curtain on those. We're going to show you
exactly how you can get powerful private AI running
today using the gear you've already got sitting
right there. OK. So let's unpack that first part.
The motivation. Why bother downloading software,
maybe messing with the terminal, when you can
just open a chat window in your browser? It seems
easier. The sources we looked at lay out six
pretty compelling reasons. Yeah. And the first
one hits you right away. Cost. Yeah. Once you
download that initial model file, that's it.
It costs nothing more to run. forever. No monthly
fees, no paying per query, none of that. The
only real cost is like a tiny bit of electricity.
And tied right into that is getting away from
limits, right? Every commercial service puts
caps on you, especially if you use it a lot.
Exactly. With these local models, you could run
a thousand queries in an hour if you wanted.
The AI itself never tells you, nope, you've hit
your limit for today. But privacy, you mentioned
control. That seems like the really big one for
a lot of people. Oh, it's huge. Probably the
biggest driver. See, when you chat online, your
conversation, your prompts, your data, it all
goes to someone else's server, who knows where,
running it locally. Everything stays right there
on your machine, 100%. Your secret company plans,
your personal journal ideas, whatever it is,
it doesn't leave. that peace of mind. You can't
really put a price on it. And then there's just
the practical side. You get actual offline capability.
I mean, you could be on a plane, no Wi -Fi, or
maybe somewhere remote. The AI just works anywhere.
Yeah, that's super useful. And you also get version
control. This is kind of neat. If you find a
model version that just works for you, gets your
style, you can keep that exact version indefinitely.
You're not forced into updates that might suddenly
change how the AI responds, which definitely
happens with the big online ones. Right. And
the last one is customization. This sounds more
advanced, but really powerful. It is. You can
actually fine tune these models. Yeah. That means
you could say, teach it all the specific jargon
for your industry, or even train it to mimic
your personal writing style. That kind of deep,
personalized training. Just impossible with the
closed off commercial models. OK, so that covers
the why. But there's always that nagging question.
Are these local models actually any good? I remember
trying some early ones, and they were. Well.
Not great. Yeah, they used to be pretty basic.
Kind of dumb, honestly. Is that still the case
or is that just a myth now? That myth is completely
busted. Seriously, the open source world is moving
incredibly fast. We're seeing models released
almost daily that often match or even beat older
systems like, say, GPT 3 .5. And the crucial
part is they run fine on regular laptops, MacBooks,
Windows machines. You don't need some monster
gaming rig anymore. OK, so if they're powerful
now and free and limitless. What's the catch?
What's the main trade -off compared to just using
a cloud service? The trade -off really comes
down to managing your own computer's memory.
That's the main constraint. Right. Memory. That
brings us perfectly into the next bit. We need
to understand what an AI model is file -wise
to get why memory matters so much. Okay, yeah.
So an AI model, at its core, it's just a file.
A really, really big file. Think of it like stacking
billions of tiny Lego blocks made of data. This
file contains billions, literally billions of
numbers. We call them parameters or sometimes
weights. These numbers represent everything the
AI will learn during its training. All the patterns,
the connections. So when you download an eight
billion parameter model, you're grabbing a file
with eight billion of these numbers. It's chunky.
Billions of numbers, okay. And you need something
special to actually read and, well, run that
giant file. That's where Olama comes in. Exactly.
If the model file is like that super -dense complex
sheet music, Alama is the specialized music player
designed just for AI scores. It does three main
jobs really well. First, it's a downloader. It
knows how to handle fetching these enormous multi
-gigabyte files reliably. Second, it's the engine.
It takes those billions of parameters and loads
them into your computer's active memory so the
AI can actually think. And third, and this is
kind of cool for flexibility, it acts as an interface.
It quietly starts up a sort of hidden software
door on your computer. Technically, it's an API
server running on local host. This door lets
other applications on your machine talk directly
to the AI model that a llama is running. Okay,
that interface part sounds important for later,
but you mentioned memory. And there's a key difference
depending on the type of computer someone has,
right? Mac versus Windows. Yes, absolutely critical
distinction here. It changes how much memory
is actually available for the AI. So if you're
on a Mac with an Apple Silicon chip, M1, M2,
M3, whatever you have, what's called unified
memory, this is great for AI. It means the main
processor, CPU, and the graphics processor, GPU,
share the same pool of RAM. So if your MacBook
has, say, 16 gigabytes of RAM total, pretty much
all 16 gigs can potentially be used by the AI
model. It's simpler. OK. If you're on a typical
Windows PC, especially one with a dedicated Nvidia
graphics card, things are different. You usually
have your main system RAM, maybe 16 or 32 gigs.
And then crucially, the graphics card has its
own separate memory called VRAM or VideORAM.
For AI tasks, that VRAM on the graphics card
is the golden ticket. It's much faster for the
parallel processing AI needs. So ideally, the
entire AI model needs to fit into that graphics
card's VRAM. That's often the limiting factor
on PCs. That makes sense. The version control
aspect you mentioned earlier really resonates,
too. I have to admit, I still wrestle sometimes
with prompt drift when you're talking to an AI.
And halfway through, it just seems to forget
what you asked it to do initially. It's frustrating.
So I'm genuinely grateful these local models
let you just stop and restart with a clean, predictable
version whenever you need that consistency back.
So OK, let's make it concrete. Someone wants
to try this. What's the simplest way to get started?
Easiest path. First, Download Aulama from their
website. Install it like any other app. Then
you open up your terminal. Yeah, that black command
line window. Don't be scared. And you just type
one single command. Aulama run Aulama 3 .8b.
Hit enter. Aulama run Aulama 3 .8b. And what
does that do? That tells Aulama. Go find the
model named Aulama 3 with 8 billion parameters.
Download it if you don't have it and then run
it. you'll start downloading. That specific model,
Llama 3 .8b, is a fantastic starting point. Very
capable, but the file size is manageable only
about 4 .7 gigabytes. Most modern machines can
handle that download and have enough memory to
run it. OK, 4 .7 gigs is manageable, but these
model files are still pretty big. If you start
downloading a few, you could eat up, I don't
know, tens, maybe hundreds of gigs pretty fast.
How do you, like, keep track of what you've installed
and clean things up? Good question. Allama has
simple commands for that too. You can type AllamaList
to see all the models you've downloaded and their
sizes. And if you want to remove one to free
up space, just use AllamaARM followed by the
model name, like AllamaARM Allama3 .8b. Easy
to manage. OK, easy enough. AllamaList and AllamaARM
got it. But hang on. You said the 8 billion parameter
model is 4 .7 gigabytes. If a parameter is a
number and there are 8 billion of them, shouldn't
the file be much, much bigger? How does it work?
That brings us to the real magic trick of running
modern AI locally. It's a technique called quantization.
This is the absolute key that lets these huge
powerful models shrink down enough to fit onto
regular computers. Quantization. Okay, what does
it do? Basically, it takes all those billions
of very precise numbers inside the model. 13
.4159265, and it makes them less precise. It
might round them down to something simpler. It's
just 13 .4. Think of it like image compression.
You know how you can take a massive, super detailed
raw photo file, maybe 100 megabytes, and save
it as a JPEG that's only like five megabytes?
Quantization is doing something similar. But
for the AI's knowledge, it's a kind of lossy
compression, but highly optimized for these neural
networks. Lossy compression. Doesn't that mean
you're losing information? Is the AI getting
dumber when you quantize it? That's the amazing
part. You do lose a tiny bit of precision, yes.
But the trade -off is incredible. Quantized models
can shrink by 50, 60, even 70 % in file size,
but they typically only lose maybe 10 to 20 %
of their raw performance score on benchmarks,
sometimes even less. Wow. It's a fantastic deal.
You get a model that's drastically smaller and
needs way less memory, but it's still incredibly
smart. That's why that 8 billion parameter Lama
3 model ends up being only 4 .7 gigs instead
of like 16 or 30 gigs. Whoa. OK, hold on. If
you can shrink models that much with only a small
performance hit. You could potentially take something
massive, like a 70 billion parameter model, quantize
it, and maybe actually run it on a high -end
laptop. Exactly. That's happening right now.
People are running quantized 70B models on Macs
with enough unified memory or PCs with beefy
graphics cards. It completely changes the game,
democratizing access to really powerful AI. It's
not just for giant data centers anymore. Is that
10 -20 % performance loss ever really noticeable
though? Like if you're asking it to do something
really complex or creative? Honestly, for most
everyday tasks, writing emails, summarizing articles,
brainstorming ideas, even coding, help you likely
won't notice the difference between a quantized
model and the full precision original. Maybe
if you were doing highly specialized scientific
modeling or something requiring extreme numerical
accuracy, you might stick with a full -size one.
But for 95 % of us, the efficiency gained from
chronization is absolutely worth that tiny dip
in performance. OK, that makes sense. So assuming
we're using these standard quantized models that
Alama usually downloads by default, can we give
people some simple guidelines for picking a model
based on their computer's memory? Yeah, definitely.
Simple rules of thumb work pretty well here.
If your machine has about 8 gigabytes of available
RAM or VRAM, if you're on that NVIDIA PC, you
should probably stick to the smaller models,
like 7 billion or 8 billion parameters. So that
Llama 3 .8b we keep mentioning is perfect. Got
it. Eight gigs? Stick to 7 or 8b. What about
more memory? If you've got 16 gigabytes of RAM
or VRAM, you can comfortably run larger models
like 13 billion or even up to around 16 billion
parameters. For coders, a great one in that range
is DeepSeaCoder -V2 .16b. It's specifically trained
for programming tasks and it's really impressive.
Nice. DeepSeq Coder dashed v2 .16b for 16 gigs.
And for the power users, people with 32 gigs
or more. Ah, now you're talking. With 32 gigs
or more, you can start running the real heavy
hitters. You can handle 34 billion parameter
models, or even the big 70 billion ones like
Lama 3 .70b. And another interesting one to try,
maybe in the smaller size like 7b, is Wizard
LM 2 .7b. It's known for being less, uh, censored
or aligned. than some others. It gives more direct,
sometimes unfiltered answers, which can be useful
depending on what you're doing. OK. Lots of options.
But running commands in the terminal is cool
for setup, maybe for scripting, but for just
chatting with the AI day to day. Most people
probably want something friendlier, a nice interface.
How do we get that? Right. You want a proper
chat window, history settings, all that. That's
where tools like LM Studio come in. or other
similar apps, there are a few now. Ellen Studio
basically provides that polished graphical user
interface, a GUI, that sits on top of your local
models. It's a dedicated chat program. It's really
good because it often shows you helpful info,
like how much CPU or RAM your AI is using while
it's thinking, and makes it super easy to just
switch between the different models you've downloaded
with a click. Okay, Ellen Studio sounds like
the answer, but if we already downloaded our
models using Olama and Olama is running the engine,
We don't want LM Studio to download everything
all over again, right? That wastes space. Exactly.
You want to avoid doubling up. The smartest way
is to install LM Studio, but then configure it
to talk to the Elama server that's already running
on your machine. Remember that hidden door Elama
opens, LM Studio can just connect directly to
that. Most of these GUI tools have a setting
somewhere to point it in an existing Alama instance.
You tell LM Studio, hey, don't download models
yourself, just talk to Alama at localhost .11434.
Then boom, all the models you got with Alama
just appear in LM Studio's chat interface, ready
to use. No redundant downloads. Perfect. So connect
LM Studio to the running Alama. That's the efficient
path. Now, once you're set up with a nice interface,
the quality of what you get out still depends
heavily on what you put in, right? Good prompting
is still key. Oh, absolutely. Garbage in, garbage
out still applies, even with powerful local models.
The sources we looked at really hammered this
point. You need detailed prompts. They suggest
focusing on defining five key things for the
AI. It's role, the specific task you want done,
the overall goal, the reason why, and the desired
tone. Role, task, goal, reason, tone. OK. If
you just say, write an email, you'll get something
generic. But if you structure it like, OK, act
as a professional employee, that's the role.
Your task is to write an email draft to my manager,
whose name is Sarah. The goal is to politely
request a two -day extension on the quarterly
report. The reason is that the final sales data
only arrived this morning. Please maintain a
polite but confident tone. Much more specific.
Way more specific. And you'll get a much, much
better, more tailored result almost every time.
That level of detail really guides the AI effectively.
OK, good prompting advice. Now, what about troubleshooting?
People trying this for the first time might run
into things that seem weird. Let's normalize
a couple of common experiences. First one, your
computer might suddenly sound like a jet engine
and feel pretty warm. Yeah, expect that. Running
these AI models, especially the bigger ones,
is computationally intensive. It uses a lot of
processing power, often on the GPU. So your computer's
fans are going to spin up fast. You'll hear them.
The machine might get noticeably warm. That's
just the sign it's doing the heavy lifting required.
It's totally normal. Don't panic. OK. Loud fans
and heat are normal. Good to know. Second thing,
the very first time you ask a newly loaded model
a question, it might seem really slow to answer,
like maybe 20 or 30 seconds of silence. Mm -hmm.
That also happens, and it's expected. That first
query involves a llama loading all those billions
of parameters from your storage drive into the
active RAM or VRAM. That takes time. Think of
it as the AI waking up or warming up. Once it's
loaded into memory, though, Any subsequent questions
you ask in that same session should get much,
much faster responses. Usually just a few seconds.
Right. So be patient with that first prompt.
Exactly. And just a reminder about storage, keep
an eye on those file sizes with a Llama list.
Clean out models you're not actively using with
a Llama ROM. And really, the final piece of advice
is just experiment. Try different models. See
how Llama 3 feels for general writing. Then switch
to DeepSeq for some coding. Maybe try Wizard
LN2. if you want less filtered responses. Find
the personality and skills that best fit what
you need to do. That seems like a great place
to land. Yeah. If we just zoom out for a second,
think about what we've covered. The core achievement
here is pretty profound, actually. You, the listener,
now have the practical knowledge to harness really
powerful AI tools completely for free, privately,
on your own machine, offline, if you need to
be, with total control over which version you
use and absolutely no usage limits imposed by
anyone else. You've essentially bypassed the
dependence on those big centralized cloud providers
for this capability. It really is a feeling of
taking back control, self -sovereignty over your
computing in a way. The future of AI isn't just
happening out there in the cloud. It's shifting
rapidly onto the hardware you own. So don't just
listen to us talk about it. Take the advice from
the source material. Go download Elama. Open
that terminal, type Elama run Elama 3 .8b, and
start exploring today. And maybe a final thought
to chew on. We talked about quantization, right?
How losing a tiny bit of numerical precision
gives us these massive gains in efficiency, letting
big models run locally. It makes you wonder,
what happens next? What will the next generation
of clever lossy compression techniques for AI
models look like? Could we reach a point where
we can not just run, but maybe even train surprisingly
large models entirely on consumer -grade hardware?
That's something interesting to consider as you
start your journey.