Gus Martins: Hi everyone. Uh, can you hear me? Yes, you can hear me. Hi. Sorry. Sorry we're 1 minute late. I'll try to do my best to finish earlier so my friend can do a pretty cool demos for you. I'm Gus. This is Ian. We are from the uh Google deep mind and I'm specifically I work on the Gemma product. Do any of you know what Gemma models are? Okay, perfect. Perfect. Thank you very much.

Gus Martins: Uh so today we're going to talk a little bit about ownership and open models. Uh and well you know who we are. But the idea is uh last Thursday we released our new uh family of models Gemma 4. And I'm going to talk a little bit about them. Uh there's going to be more information tomorrow in the keynote by Omar and there's another talk by CassD also tomorrow that she go into even more details. We're going to tell a little bit of the story but the story is a little bit bigger. We'll try our best here.

Gus Martins: Uh so why does it matter? Uh if you ask me I work for Google of course. If you ask me what's the best model for you to try the easiest one I will answer for you. Gemini. Gemini is the best model we have. pretty strong multi model can do all kinds of things. But then there is uh there's more to this story than just having the strongest model possible. In some situations, you want to own the model. You want to be able to run on your own hardware. You want to customize it. You want to be able to send your proprietary data that cannot leave your infrastructure. So there are many situations where even the best proprietary model will not be able to help you directly. That's when you might need an open model. That's where Gemma comes in. So when you think why does Google have two family of models? Because they complement each other. So Gemini is the most intelligent one. Can do a lot of cool stuff but it's hosted in Google servers. You need the API to access. If you need more control and access, you need an open model. That's why we have Gemma. That's why uh and and we are very proud that the quality is very very strong. We're going to go some details later but the idea is you would be able to do a lot of cool stuff with it.

Gus Martins: Among the launches we did we released four sizes. Uh two are target to mobile let's say like that or or IoT or smaller devices. It's a E2B and a E4B. Uh these names is a little bit weird. We are the only ones that use this name. Uh and the E stands for effective. And the idea here is uh the model uh uses as much as 2B uh what a 2B model would use as memory but it's larger than that. The 2B is around 5B uh uh parameters. But then you say oh but where is these other 3B in memory the the fun fact is that they are not really parameters from the transformers. They are uh like mapping uh tokens. So you can leave them in other memory. So what you really need on your GPU memory is the 2 billion or the 4 billion. Why do we do that? So that you can run these models on a phone on a Pixel phone or B any phone you have there. You can run these models and they're very strong. The E2B E4B both of them have a text uh vision and audio input and they do only text output. They can do thinking, they can do coding, function calling, all this kind of cool things. This all running on your phone right now. You could download it right now. Right.

Gus Martins: We also have other two models which are the larger ones. We have a 26B and a 31B. The 26 is a mixture of experts which means that uh it's as if we had many other models working together where where each of the where each one of those are like a 4B model. Why does it matter? Because it it has 26 billion parameters but it needs a space of a four billion parame to do the work. And this makes uh makes it accessible to way more hardware to way more people and it's still pretty strong. But our strongest model is the 31B dense which is 31 billion parameters model. And this is really really strong. If we look into our uh ELO score on LM Marina, you can see that both our models are they are I guess now they're fourth and seventh as the leads on open source models open models. And if you compare them to maybe the top 20 30 all of them are at least twice three times larger than our models in some cases 20 times larger. So we are talking about a disproportionate amount of intelligence per size. So our 31B model is the one I use very regularly. It can do basically anything from coging aic everything multilingual all of that. So I strongly recommend you try those. They are so strong that they are uh all both of them are really good to use on your on as a cloud deployed model. They can run on your desktop but if you use on your server as your endpoint to do uh your work they are pretty good.

Gus Martins: And you ask is this the most intelligent model? No it isn't. I'm going I'm I'm very biased and I love them but I know the capabilities. But the question is, do you need the most intelligent model of the planet to summarize your mail, to do some more minial tasks, to help you code, to do some agentic capabilities that are searching and interacting with docs? Probably not. That's why these models are so strong because they're cheaper. They're very strong, but they're cheaper to run. They require way less hardware. a 31B running one GPU. The competitors need 200 GB of memory which would be maybe four or five GPUs. So you can see that the price here is really really different. One easy place for you to try these models is on AI.dev where you can try Gemini models VO all the other mods but G gem are there both 26 and 31B you can try right now. They are free. You can play with it. uh and they can do I was I was going to show a demo but I can't now but they can do some cool stuff which is vision plus thinking plus code execution all at the same time right uh I try to post something about this later but the ideas you can play with the models pretty easily and right there right not now finish let's finish the the talk and then you play with it uh and as I was saying the the intelligence per parameter that these models bring is pretty good it's very very strong and if We use the ALO score for Lemarina because it's a benchmark that's a person's preference, right? We can look into academic benchmarks. They are very very strong. But the how the model responds to your queries, that's very important, right? That's how your customers will see how you will see and and interact with it. So this is why this is so important.

Gus Martins: And why does all this matter? One of the reasons that we care so much is because uh you want to you want to the user to have ownership and more than that we are enabling sovereignty and sovereignty means in terms of you own the model and you can adapt your use cases and you you are not susceptible to I don't know loss of service or for some kind of uh someone saying no you cannot use this model anymore it's all available to you and one of the changes we made last year until Gemma and others. We had our specific license, a Gemma license, which is pretty good, commercial friendly and all, but there's a problem. If you have a custom license, I don't know if you have any lawyers here. Uh, if I tell you, oh, we have this custom license, your lawyers will look at me with that face that I hate you, Gus, and then they will spend like 18 months doing procurement process to understand the license and trying to change. And that never works. So, it's pretty hard for sovereign institutions to adopt this kind of thing. That's why we move to a pass 2.0 you for for Gemma 4 and going forward and that makes thank you and that makes our life m your life much easier to to convince your legal department let's say like that that look we own this model we can use so this is pretty important and it enables many many uh sovereign institution to use our models we have some examples for example here for example uh Ukraine use Gemma to parts of their uh services we have a one version of the Gemma model that was fine-tuned for Bulgarian it was their uh LLM for the country that was based on Gemma 2. We are working to make sure they use Gemma 4. Now we also have a Brazilian version that is based on Gemma 3 was fine-tuned for Portuguese.

Gus Martins: And the challenge of these models today is that they if you want to fine-tune Gemma to a specific language, it's becoming very hard to do that. And the problem is hard because not the the tooling or anything because the model is pretty strong on those languages already. So any gains you try to have you might not get there. So you might spend a lot of time to get like 1%. And then maybe I don't know if it's the best use of your uh time. So this is good and bad at the same time because but it's good that you can automatically use in many language you can try right now and if you go into uh the marina for languages in many languages are top two three and look it's a 31B model. It's very very small right? So this is pretty good. That being said, uh I will let my colleague continue and show some demos.

Ian Ballantyne: Thank you, guests. So, uh one thing that's I think is really important about these models is that when you think about using open models, you think about like using proprietary models. We're move we're seeing a shift now to more kind of agentic capabilities and the kind of tasks that we're trying to do. And with that comes a cost in tokens and token generation. So one benefit of uh taking ownership of the models is your ability to control or in cases where you have sunken hardware cost uh to be able to iterate on top of that. Uh this graph on the right hand side is from the state of AI report that open router did and it shows the uh it's a bit small for you on this diagram but have a look at that link. Uh shows the different types of tasks that people are doing through open router at the moment and you'll see that the the one that's about here. This one here is programming is right in the middle and this is kind among some of the highest tasks in terms of token generation both input and output combined. So the more we have agents work and do these kind of tasks for us that have very high token generation costs that's when you start to get more benefit from being able to take control of that in itself.

Ian Ballantyne: So if for instance you have a laptop that is capable of doing a particular task that you need to be doing like processing a document or analyzing some data or doing some research or in the cases Gus talked about doing some coding that's suitable for that then you have a GPU that you can take advantage to do some of that stuff. Now similarity similar to what Gus said about you know we don't necessarily we still have frontier models for doing the best possible things. I wouldn't get this model to do like a you know a full systems architecture and redesign of your application right it's not kind of for that but what it is very good at doing is following very specific instructions about doing things like refactoring analyzing uh generating code in uh in small modular bits and you can offload a chunk of work in that style to these kind of models to be able to do that whether it's on a single GPU or on your own personal hardware um and the way that we kind of think about This is like a a set of thresholds like when do we get to the point where these models are capable of doing the task but then they also fit on the right hardware that they also uh can do it with the right amount of latency depending on the that if it's a task for a user needs to happen in a couple seconds if it's a task where you're doing things like batch processing you maybe have slightly different thresholds for like what needs to be done um and then also what the cost of actually doing that is so if you have a sunken cost in terms of like infrastructure that you already own or that you're prepared to uh outlay and then operating on that or whether you're leasing like GPU time or something else like that. So these are going to be very specific to the task that you're trying to achieve. But what uh what you can do with open models is you can think very carefully about like what which of these tasks can I can I fully offload or can I fully own compared to relying just on using the best possible models to do uh that in the cloud.

Ian Ballantyne: and uh an example. So Gus talked about the different types of hardware that can run these things. Now I'm just going to run this little demo in the side at the moment. So we now have models that will work directly on mobile and edge devices. Uh this example here built by Coremax team. Um is a set of agent skills that the model is running on a phone. So uh I'm going to mute the microphone for that. Um, so you can talk to the model, you can show it images, uh, you can show it the world around you, and you can prompt it and chat to it. And what this one is showing is that it can look through a set of skills that it has about things on the phone. So either it can take actions on the device itself. So trigger other applications like trigger calendar apps, trigger maps apps, or you can kind of define your own skill sets. And what's different now with the Gemma 4 models than we saw for the previous generation is that it's able to reason about what actions it needs to take and reliably uh make those function calls defined. So what this app will allow you to do is is kind of acts as like a playground. Uh so this is Google AI gallery and you can find it on iOS and Android and you can experiment to see what the models of this size are actually able to do. So I think this is the two billion parameter model, but there's also the 4 billion parameter model depending on uh the size of your hardware.

Ian Ballantyne: And when we get to desktops and single GPUs, as Gus mentioned, that's where you can use the uh the 26 and the 31B models again on your local hardware. And I'll show you how to do that in a minute. But there's one kind of key point here is whereas we're not paying for uh the price of these agents or models within tokens, we're actually paying for them in terms of energy costs. If we think about it, because now you're thinking about utilization of GPUs, you're thinking about utilization of MPUs on the hardware itself. Uh when are you going to do these tasks? Does the user need to get a response right now when you're taking a picture of something or is it something that you can process offline as a background task when they plug their phone in at night? So the what I'm trying to say here is that the thresholds and how you think about the usage of these models kind of shifts when you come to ondevice or ownership because you think more about how theyre being executed and why theyre being executed.

Ian Ballantyne: Um yeah, perfect. Uh and similarly on the enterprise side, if you don't have a piece of hardware that can you know run the 31 billion parameter model, you can now be thinking about scaling that down. So maybe if you wanted to use a 300 plus uh billion parameter model before you might have need multiple GPUs. Now you can think about using a single uh H100 or A100 or even in some cases like an L4. And then the costs obviously related to that also kind of go down. So again it's a calculation that you'll have to do depending on your use cases. But there's ways that you could scale. For instance, running one of these models to serve, you know, a small team or to serve a company um depending on uh what you're trying to do. And the and the and the final point is that you also have the fine-tuning component too, which is that because these models can be customized, you can deploy your own version of it. So for instance, we have a variant of Gemma models called Medgema, which is specialized for medical use cases. So if you wanted to have something that would operate on private data that you can control yourself, you can now re feasibly deploy this to like one or maybe two GPUs uh to run that for I don't know like a whole hospital for instance. So these are kind of worth considering for the enterprise case.

Ian Ballantyne: Uh I'm going to jump straight to demos now. Um I shown you some demos on the phone. I'm going to show you a quick demo uh here. Who quick show of hands. Who's ever used uh a tool called LM Studio? Okay, just under half people. So, LM Studio is a way that you can play around with local models. And I have here I have this is the 26B model. So, this is our faster of the the two larger models with four billion activated parameters. And I'm at the moment, including the context, it's probably about 26 GB in RAM. And this is an M4 Mac. So, I've got unified memory. I've got up to about 48 gigabytes. Uh, so I can run it on this machine. And I'm just going to run this terminal right here. Let's give that a go. Oops. Pre-showing my demo. Let's try that again.

Ian Ballantyne: There we go. So, I'm just going to run a little process where I'm going to do some quick a trick quick translation on my device. So, what it's going to do is I've got an orchestrator on this side here, uh, which is going to hopefully kick off my agent in a minute. Let's make sure we are loaded. Let's see what LM Studio is doing. Yeah, it's just processing at the moment. And then it's going to farm out uh, this translation to all of these different uh, uh, windows and each one of them represents a different sub aent. So, this is running on my device uh, and it's going to basically execute all these translations in one go. So I've given it like the Gemma 4 announcement and I just want to want to translate to all these different languages. So you'll see in a second it should hopefully send it over there. 3 two one and hopefully we should be generating translations a second. There we go.

Ian Ballantyne: So so you can imagine doing any kind of aentic task on your local machine. uh you could have it like processing files, you could have it doing additional analysis and hopefully what you'll see in a minute is it will be able to compile all these back and then it will generate me a quick web page and then you can see the results through the translation. There you go. So there's the multilinguality of the model there as well.

Ian Ballantyne: Okay. Uh right. Uh so in the interest of time uh I just want to say that the the main next step for exploring and trying out these models is as simple as this code on the right hand side. Um you can take any open AI compatible interface that you've got and you can point it at a service like Olama or LM LM LM Studio and you can just pick out the gemma model and that's all you need to change codewise to at least uh try it out. So the first thing we recommend you do is to drop it into existing workflows that you have to then see what the model can handle. Like what is it working well at? What would it need tuning for? What is kind of out of its depth in terms of like the complexity of the task.

Ian Ballantyne: Um next is to kind of bolster your evaluation suites because you know benchmarks are great and everything for just saying what general capabilities are but the reality is that how good the model is depends on how well it does on your task and not anybody else's task. Um, the other thing I mentioned very briefly is thinking about how you actually serve these models in the end. So if you need to run your own GPU, you need to host it. Yes, you're in control of like uptown uptime and downtime, but then there's like maintenance costs and other stuff like that. So you have to be you have to consider that as like one of the factors like the ongoing costs as well as well as any upfront capex costs if you buy infrastructure or hardware to do that too.

Ian Ballantyne: Um on mobile devices for instance you have to think about like if I'm going to offload stuff to a phone like what am I supporting? What accelerators do they have? What size RAM do they have? So the conversation becomes a little bit more complex but then there's a whole heap of things you can unlock like working offline or working on users private data that never leaves their device. Um and finally if you want to scale this up to enterprise levels you have to think then again about like the kind of infrastructure that you're running on and what the ongoing costs are of that as well. But it does kind of unlock that. So, uh, with that, the summary is that you can use these models in pretty much any way you can think about. Experiment what kind of tasks are possible with it. Use some of the benchmarks to kind of give you an indication of like what's feasible. But, uh, really we want to hear your feedback and how you get on with these and and how you fine-tune them and what kind of things you run into. And, uh, we want to help you on that journey as well. So, with that, thank you very much.