Look at any major laptop maker today and you will realise that they have been doubling down on AI. We have AI features, dedicated NPUs, LLMs and even AI agents making their way into our devices lately. But, what most people don’t understand is that there is a difference between having an AI PC and needing something to run some massive AI workloads. If all you want to do is occasionally use an AI assistant, experiment with a small local model, or take advantage of the AI features built into Windows and other apps, you can just go and buy any AI laptop with an NPU in the market. But, when you plan on running local AI on your laptop, things change.
So, when do you actually need an Nvidia RTX GPU in your laptop for AI? And when is an NPU-equipped laptop more than enough? Let me break it down.
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Ask yourself a simple question- Do you need an AI PC, or do you need to run massive AI models locally on your device?
If you only want to use an AI assistant or a few AI features in your everyday apps, an NPU-equipped laptop may be enough. But if you want to generate images and videos locally, experiment with larger models, or run more complex AI workflows without relying on the cloud, a dedicated GPU starts making much more sense.
This is because demanding AI models need memory as well as raw processing power. A dedicated GPU gives you both, with its own pool of VRAM that can be used to load models and process demanding workloads.
Getting further into details, say you want to generate AI images locally on your device. Tools such as ComfyUI let you build complicated image-gen workflows instead of simply entering a prompt and waiting for a result. As the models and workflows get more demanding, GPU performance and, more importantly, dedicated VRAM become super important.
Talking about the software side of things, Nvidia RTX GPUs support CUDA and feature dedicated Tensor Cores, allowing applications that support Nvidia’s ecosystem to take advantage of the hardware for AI workloads.
One such application is LTX Desktop. In the case of this app, CUDA support is needed for local generation on Windows. Its current requirements call for an Nvidia GPU with CUDA support and at least 16GB of VRAM for local video generation on Windows. It also recommends at least 16GB of system RAM, with 32GB being the recommended configuration.
Now the above examples do not mean that every AI application requires an Nvidia GPU. What they do show is that you should check the requirements of the software and models you actually want to use before buying a laptop.
So how should you choose your Nvidia RTX-powered laptop? Before answering that, let me tell you about a personal experience.
I installed LTX Desktop on my Alienware 16 X Aurora in hopes of trying out video generation. But, it didn’t run locally and asked me to input an API. This was surprising as my Alienware has an Intel Core Ultra 9 chip and Nvidia RTX 5070 laptop GPU which is more than enough. Right? Wrong.
My laptop also has 8GB VRAM. And LTX Desktop, as mentioned earlier, needs at least 16GB. So, you should definitely check the requirements of the software and models you actually want to use before picking your laptop. The name of the GPU alone does not tell you everything.
Let’s start with VRAM. Nvidia’s current RTX 50-series laptop range gives you several options. The RTX 5050, RTX 5060 and RTX 5070 Laptop GPUs come with 8GB of GDDR7 memory, while the RTX 5070 Ti has 12GB. The RTX 5080 has 16GB and the RTX 5090 goes all the way up to 24GB.
More VRAM gives you more room to load larger models and work with more demanding AI workflows. This does not mean you need to go out and buy an RTX 5090 laptop. For most users, that would be excessive. But if you already know that you want to experiment with demanding local AI models, having more VRAM can give you considerably more flexibility.
You should also pay attention to the exact laptop implementation of the GPU. Two laptops can carry the same RTX GPU but deliver different levels of performance depending on factors such as power limits and cooling. So don’t just look at the RTX 5070 or RTX 5080 badge, check the specifications of the exact SKU you are planning on buying.
There is also one misconception I would like to clear up here. Buying a laptop with an Nvidia RTX GPU for AI does not automatically mean buying a huge gaming machine.
There are now several options that offer a great balance between portability and performance. For instance, machines like the Lenovo Legion 7i, Asus ROG Zephyrus G14 and HP Omen 14 give you some great performance in a relatively slimmer form factor.
So which laptop will you be picking and what kind of local AI are you planning to run? Do let us know and keep watching this space for more such updates.
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