The Razer x Lambda 'Tensorbook' laptop has been unveiled as a collaboration system from the two brands to offer professionals with a way to seamlessly handle deep learning and machine learning processes.
The laptop boasts an NVIDIA GeForce RTX 3080 Max-Q graphics processor and optional support for dual-booting into Windows. The unit comes preloaded with Linux along with Lambda's deep learning software and the Lambda Cloud to support engineers as they create, train and test models on a local basis.
Co-Founder and CEO of Lambda Stephen Balaban spoke on the new Razer x Lambda 'Tensorbook' laptop in a blog post saying, "Most ML engineers don’t have a dedicated GPU laptop, which forces them to use shared resources on a remote machine, slowing down their development cycle. When you’re stuck SSHing into a remote server, you don’t have any of your local data or code and even have a hard time demoing your model to colleagues. The Razer x Lambda Tensorbook solves this."
Deep Learning Laptop Models
The Razer x Lambda 'Tensorbook' Supports Work in Machine Learning
Trend Themes
1. Deep Learning Laptops - The trend of laptops specifically designed for AI development in machine learning and deep learning will continue to grow.
2. Dual-boot Laptops - The trend of laptops that can boot into both Windows and Linux will continue to grow as more professionals require flexibility in their development environments.
3. Local Machine Learning Development - The trend of local machine learning development will continue to grow as professionals seek to increase development speed and maintain control over their data.
Industry Implications
1. Laptop Manufacturing - Laptop manufacturers have the opportunity to specialize in creating laptops specifically designed for AI development in machine learning and deep learning.
2. Cloud Computing - Cloud computing providers have the opportunity to develop platforms designed to support professionals in their machine learning development efforts.
3. Software Development - Software development companies have the opportunity to create tools and platforms designed to support local machine learning development and increase development speed.