Why I’d never buy an Android phone with 8GB RAM in 2026

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Whether you’re buying a high-end PC or the latest flagship smartphone , RAM is a precious commodity these days. The crunch in global supply has driven prices to record highs, making it harder and more expensive to get our hands on cutting-edge memory at a reasonable price.

At the same time, the market is pushing AI features on us at an accelerating pace, and, as you probably know, on-device AI tools require a lot of RAM. If you didn’t, the problem is quite simple: local models running on your phone (or laptop) rather than in the cloud have to be loaded into RAM to run at an acceptable speed.

The advantage is that these AI features can work faster, offer better privacy, and often function without an internet connection. Google’s tools, like Magic Compose, Call Notes, and app-aware Magic Cue, are designed to eliminate busywork from daily mobile tasks. Rather than waiting for every request to travel to a remote server and back, on-device models can begin processing tasks immediately, reducing latency and making interactions feel more seamless.

Running AI locally also has advantages beyond speed. Keeping models on-device means sensitive information doesn’t leave your phone, improving privacy and helping to keep your data your own. It also reduces the cost of processing every request in a cloud data center, which is becoming increasingly expensive , and allows us to use lightweight AI tools for free.

This, apparently, is the future of smartphones — AI will work for us via everyday language requests rather than manually swiping through menus ourselves. The trade-off is that these benefits require several gigabytes of RAM to keep models loaded and ready to respond at a moment’s notice.

With that in mind, we’re stuck between rising costs and requirements; buying a phone with 8GB of RAM — a previously very safe value — is no longer enough to use the latest and greatest features phones offer.

You only have to look at Google’s recent Pixel product line to see what a headache it’s been sifting through which models can and can’t support the latest AI tools, such as the Gemini Nano 4 . Those Gemma 4 E2B and E4B variants are sized at 4.2GB and 5.9GB, respectively, meaning you’ll want a phone with 12GB of RAM or more to house your other apps. Importantly, those requirements are up from the sub-4GB memory required by Nano 3; the most powerful on-device models are becoming bigger.