in

Tecno’s First 5G Smartphone With Dimensity 900 SoC Launched


Tecno, a smartphone brand, has launched its first 5G smartphone for the market. There’s a huge display and battery inside the smartphone. Not just that, there’s even support for 120Hz refresh rate for the display, and the highlight is that the smartphone is priced in the budget segment. The name of the new 5G smartphone from Tecno is – Tecno Pova 5G. Let’s check out the smartphone’s price and specifications.

Tecno Pova 5G Specifications and Price

The Tecno Pova 5G comes with a 6.95-inch FHD+ IPS LCD display panel with support for 120Hz refresh rate. The screen size of the smartphone is humongous and might suit only a few. The Tecno Pova 5G is powered by the MediaTek Dimensity 900 SoC coupled with up to 8GB of LPDDR5 RAM and 128GB of UFS 3.1 internal storage.

The smartphone runs on HiOS based on Android 11 out of the box. Further, the device comes with up to 3GB of virtual RAM. The company has also given the smartphone a fingerprint sensor at its side to provide additional security features to the users.

In the camera department, there’s a triple-camera setup at the rear, where the primary sensor comes with a 50MP camera paired with a 2MP sensor and an AI lens. For selfies and video calling, there’s a 12MP sensor at the front. The smartphone’s camera offers features such as 4K time-lapse and panorama, and more.

There’s a massive 6,000mAh battery inside with support for 18W fast charging. Fast charging is an area where the device disappoints a little. However, the company has claimed that a 15-minute charging would offer 3 hours of gameplay time to the users. While it sounds shady, nothing can be said before the device is put to testing.

The Tecno Pova 5G has been launched at a price of $289 or approximately Rs 21,676. This is very affordable if it launches in India around the same price point.





Source: https://telecomtalk.info/tecnos-first-5g-smartphone-with-dimensity-900/487651/

Rotating cube with hand

Flutter realtime object detection with Tensorflow Lite