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Deep Learning Laptops We’ve Reviewed (2022)

Laptops with GPU provide an additional advantage to speed up the training process and help reduce time from model training
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As an amateur professional, there are certain key components to focus on while purchasing a laptop for performing ‘deep learning’ operations such as RAM, CPU, storage and operating system. 

Laptops with higher RAM would ensure faster processing while those with GPU provide an additional advantage to speed up the training process and help reduce time from model training. Another essential component for deep learning laptops is ‘graphics card’, used to render higher dimensional images. 

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Here is a detailed list of top laptops for deep learning—

TensorBook

Lambda Labs recognises ‘Tensorbook’ as the “Deep Learning Laptop”. 

Tensorbook is equipped with GeForce RTX 3080 Max-Q 16GB GPU, VRAM-16 GB GDDR6 and is backed by Intel Core i7-11800H along with RAM of 64 GB 3200 MHz DDR4 and storage of 2 TB NVMe PCIe 4.0. 

(Image source: Amazon

According to Lambda Labs, Tensorbook’s GeForce RTX 3080 is capable of delivering model training performance up to 4x faster than Apple’s M1 Max and 10x faster than Google Colab instances. It is also equipped with pre-installed machine learning tools such as PyTorch, Tensorflow, CUDA, and cuDNN.

(Image source: Lambda Labs)

Razer Blade 15 RTX3080

Razer Blade 15 RTX3080 is an equally good choice in terms of deep learning operations. 

The laptop is powered by NVIDIA GeForce RTX 3080 Ti along with Intel Core i7-11800H. The Intel Turbo Boost Technology can boost the i7 processor up to 5.1GHz.Go with ultra-fast 360Hz FHD. 

(Image source: Amazon)

Razer Blade 15 RTX3080 has a battery life of upto 5 hours. 

The laptop efficiently dissipates heat through the evaporation and condensation of an internal fluid and keeps it running soundlessly and coolly even under intense loads owing to features like vapour chamber cooling for maximised thermal performance.

Asus Rog Strix Scar III G733

It is a powerhouse laptop with the combination of both NVIDIA and AMD. It is powered by AMD Ryzen 9 5900HX CPU and GeForce RTX 3080 GPU along with an ultrafast panel up to 300 Hz/3ms. It has a 90 Wh battery with rapid Type-c charging with video playback upto 12 hours.

(Image source: Asus)

ASUS ROG Strix G17 

The ASUS ROG Strix G17 laptop is equipped with RTX3070 GPU along with 8GB VRAM and 8-core Ryzen 9 which makes it one of the most suitable laptops for machine learning. It also has a 165Hz 3ms refresh rate and a 90Wh battery which allows usage upto a solid 10 hours.

(Image source: Asus)

 Eluktronics MAX 17

The Eluktronics MAX-17 renders itself the lightest 17.3″ gaming laptop in the industry. It is powered by Intel Core i7-10870H Eight Cores-16 Threads (2.2-5.0GHz TurboBoost) along with 8GB GDDR6 VRAM NVIDIAGeForce RTX 2070 Super (Max-P–TDP:115 Watts). 

(Image source: Eluktronics)

In terms of memory and storage configuration, the laptop is equipped with 1TB Ultra Performance PCIe NVMe SSD + 16GB DDR4 2933MHz RAM. 

ASUS TUF Gaming F17

ASUS TUF Gaming F17 is yet another impressive option for deep learning operations. It is powered by the latest 10th Gen Intel Core i7 CPUwith 8 cores and 16 threads to tear through serious gaming, streaming and heavy duty multitasking. It also has GeForce GTX 1650 Ti GPU with IPS-level displays up to 144Hz. 

(Image source: Amazon)

The laptop also features a larger 48Wh battery that allows up to 12.3 hours of video playback and upto 7.3 hours of web browsing. In terms of durability, it claims to be equipped with TUF’s signature military-grade durability. 

Razer Blade 15

The Razer Blade 15 laptop boasts of 11th Gen Intel Core i7-11800H 8 Core (2.3GHz/4.6GHz) and NVIDIA GeForce RTX 3060 along with 6GB DDR6 VRAM. 

(Image source: Amazon)

This laptop comes with a built-in 65WHr rechargeable lithium-ion polymer battery that lasts upto 6 hours.

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Sreejani Bhattacharyya
I am a technology journalist at AIM. What gets me excited is deep-diving into new-age technologies and analysing how they impact us for the greater good. Reach me at sreejani.bhattacharyya@analyticsindiamag.com

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