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Hi, in this video we are looking at

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how to explore and deploy the many models

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Foundry have to offer, and understand the different

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deployment types, as it can be quite confusing

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to be honest.

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I will show you how to deploy the

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models you have available, how to get access

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to restricted models and how to request more

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quota, so let's get started.

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If we look at the model catalogue, you

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will see there are over 10,000 LLMs

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models to choose from, but in real life,

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unless you have a very niche scenario, you

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will only be dealing with a handful of

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the model providers and the model types.

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Foundry's most known models come from OpenAI, Anthropic,

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XAI, Mistral and DeepSeek.

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The only big brand missing is Google Gemini,

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but that is to be expected, as Google

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and Microsoft are competitors in both the cloud

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hosting and the AI space.

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Microsoft themselves also have a few models on

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their own, but nothing revolutionary.

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There's one special model called the model router.

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It as such is not a model, but

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instead a collection of models that based on

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the question, can choose simpler or advanced models

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on the fly.

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I had a mixed result with this, so

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I tend to not use it personally, but

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it's an interesting idea and behind the scenes,

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you can go in and choose which models

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it have available to route to.

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Not all models are made for the same

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purpose.

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Some do text, some do images, some do

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embeddings and some do speech, so it's a

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good thing to go in and check the

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capabilities of each model as you go.

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It's also not all models that work with

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the agents and workflow services, and since those

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are the ones we are going to focus

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on in this series, we will tend to

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stick with these models as you see on

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the screen.

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Looking at a specific model, a description is

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there and a link to the pricing.

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While this screen is okay, I tend to

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instead go to the provider's own specific site

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as it gives more details and capabilities.

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Here is, for example, what GPT 5 have,

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and we can see features and pricing directly.

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I normally only use models that can do

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at least tool calling or function calling, as

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it's some kind called, and structured output, as

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I tend to use these features all the

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time.

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Beyond that, I look for at least an

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embedding model for VectorStore, and then I choose

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non-reasoning chat models and reasoning chat models

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for the more advanced workloads.

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These are my go-to models as of

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December 2025, where GPT 4.1 and 4.1-mini

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is my non-reasoning, 5.1

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and 5.1 Mini is for reasoning, and

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TextEmbedding 3.0 Small is for embeddings.

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I am slowly getting rid of GPT 4.1

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as it's quite pricey compared to how

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old it is, but it's really nice when

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you just need to set up a model

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without needing to configure all the reasoning settings

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and so on.

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Now let's dive into how to deploy a

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model, and you do that by clicking the

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model in the details page, and on the

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top, choosing the deploy button.

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When you press that, there's a default settings

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and a custom settings.

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I will very much recommend you choose custom

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settings, because if you just press default, it

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just creates the model with no questions asked,

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so it's better to go with the custom

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model, in my opinion.

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What you see is this sidebar that will

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pop up next to the deploy, where you

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give a deployment name, and this is special

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for Azure in that you can choose your

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own name for the deployment, while, for example,

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in OpenAI, if you need to reference GPT

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5.1, you need to give this name,

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but you could call this my model if

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you want to.

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But I recommend that you don't change these

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names, because these are the ones the different

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providers show in their spec sheets and so

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on.

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Next, you choose a deployment type, and there

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are four different types.

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There's something called global standards, meaning that you

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get the cheapest pricing, the pricing you normally

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see on the spec sheets, but if you

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can't rely on that, every call you do

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will stay within the Azure region that you

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have chosen.

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For a little more price, you can do

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data zone standard, which is within the Azure

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region's data zone.

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And then there is provisioned version of this,

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where you get more reliability on your models,

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but you pay by the hour for each

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of them, while global standard and data zone

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standard, you only pay when you use tokens.

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Beyond that, you can choose a model version.

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There's often only one, but some of the

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older ones have older versions, and then some

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upgrade policies that it will automatically go to

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the newest version when they become available.

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Then you choose your tokens per minute, and

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you might feel that you don't have enough

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tokens per minute, and if you don't, you

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need to go with quotas and raise them,

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as we'll talk about in a little bit.

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The final thing is guardrails.

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There's the default, the default v2, and you

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can make your own guardrails as well, which

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we'll cover in a later video.

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So once you do this, you deploy it,

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and you end up with a model.

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Certain models you can't press the deploy button

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on, and that is because some models are

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behind registration, meaning that you need to, it's

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often models that are under heavy use or

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very resource intensive, because Microsoft in general, like

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everyone else, have a hard time keeping up

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with providing servers for all this load.

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So some of them are behind a request

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access, and when you press request access, you

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are taken to a website where you fill

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out a form.

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You need to fill out your address details,

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your subscription ID, and why you need access

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to the model, and so on.

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And it's a bit of a lottery, to

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be honest.

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Sometimes I have received after requesting access, a

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day after I get access, some I never

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get an answer back.

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And even if I'm on my private system,

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or I work with a company that have

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dedicated Microsoft contacts and so on.

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The contacts I've spoken to is, say it's

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a come first, come serve model, which I

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don't believe when a Fortune 500 company comes

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along, they will probably push them up the

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length.

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I've also seen that sometimes I need to

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request, not here for a month, request again,

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and then I suddenly get it.

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So it's not like it always works.

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If you don't hear something in a couple

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of weeks, try and request again.

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That is my recommendation.

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Or at least speak to some Microsoft contacts

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that you have.

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But it is a real lottery when we

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have so few resources at the moment.

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Once you have your models, you can go

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to the build tab and then in the

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model section here, it will list your default

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different models.

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And if you press them, you go into

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the playground, but also go to the details

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where you can edit.

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So you can change the number of tokens

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and so on.

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You can delete your model, not that it

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costs you anything just by having it around.

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And again, if you find that you use

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a model and you constantly run out of

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tokens, you might want to request a quota.

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And a quota is, again, a website that

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you go to, fill out a form, tell

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why do you need it, how many thousands

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of tokens do you need instead.

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And again, this is also a lottery.

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I have tried the worst, trying to get

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more tokens for one of the models back

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in January and took three months before we

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got it.

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But I've also tried going here and the

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next day having many more tokens than I

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actually requested.

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So again, go out early getting the quota

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if you need to go live at some

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point and ask for a lot because it

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can be really, really difficult.

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And I have scenarios right now where we

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need to go to less capable models because

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we simply cannot get the quota for the

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high ones.

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But that is everything about models and model

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deployment.

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See you in the next one.
