Machine learning (ML) is one of the most popular and the fastest-evolving sectors in the sphere of modern technology. Regardless of whether you’re a computer science or data science student or a professional, selecting the right laptop for machine learning is critical. The right specification will ensure that one can deal with complex algorithms, training big data and exploring new technologies in ML. However, with so many choices, how do you choose the best laptop for machine learning?
In this article, we’ll explore the most important criteria that you should pay attention to while choosing a laptop for machine learning, and we’ll also mention some of the best models depending on performance, price, and convenience.
Evaluations to Make when Selecting Machine Learning Laptops
In this case, when buying a laptop for machine learning, you have to pay attention to the following parameters for efficient operation. These include:
1. Processor (CPU)
The processor is the most important component of any laptop and its efficiency determines the rate at which the system can execute multiple operations. The right model of processor for a machine learning task is one that has multiple cores and a very high clock speed. For machine learning operation, the most commonly used processors are Intel Core i7, i9 and, the higher version of AMD Ryzen 7 or even better AMD Ryzen 9. For machine learning, faster processors assist you in training models faster and running your experiments without the hindrance of slow processors.
2. Graphics Processing Unit (GPU)
The GPU is a crucial part for machine learning, especially for deep learning. Most of the machine learning frameworks such as TensorFlow or PyTorch, by default, are built to take advantage of the GPU for forward and backpropagation. Having a laptop with a dedicated GPU makes the training of large sets much faster. For ML students, the NVIDIA GeForce GTX or RTX series, let alone the NVIDIA Quadro or Tesla series, are ideal GPUs.
3. Memory (RAM)
RAM is essential since it helps your laptop to perform large datasets in a lesser amount of time. For machine learning students, it is recommendable to have at least 16 GB of RAM. If you are going to work with really huge amount of data or perform several analyses at the same time, it would be great to have 32GB of RAM or more. This means that for computations requiring large amounts of data, you will be able to run more data and applications from your machine’s memory.
4. Storage (SSD)
As you will be working with big data, you require easy access to your files and data in the system. An SSD (Solid State Drive) is faster than an HDD (Hard Disk Drive), and can perform data read and boot up in shorter time. It is advised to have a minimum of 512GB SSD but in case you handle big data, you can go for 1TB SSDs. The faster your storage, the better the flow of your machine learning process works.
5. Display
Although it may appear inconsequential, a good quality display can go a long way in making your experience as a machine learning student much more pleasant. At least 1080p (Full HD) display on a laptop is needed for comfortable coding and paper reading. In case of working on large and detailed visualizations or using several application simultaneously, you may opt for the larger screens size which are equal or more than 15 inches.
6. Battery Life
If you are going to be typing on the go, look at the battery life of the laptop. Training models, especially large ones, are battery hungry and can significantly reduce a Laptop’s battery life. Most of the ML work is done when the computer is plugged into the wall, but it’s nice to have a battery that will last longer for less intensive use or on the go.
The Best Laptop for Machine Learning Students for 2024
Having a clear understanding of which features to focus on, it is time to discuss some of the best laptops for machine learning students in 2024. We’ll look at a variety of budgets and features to assist you in selecting the right machine for you.
1. Apple MacBook Pro 16-inch (2021)
Specifications:
- Processor: Apple M1 Pro or M1 Max chip
- RAM: 16GB/32GB
- GPU: M1 Pro/Max: Coherent 16-core or 32-core GPU
- Storage: 512GB – 1TB SSD
- Display: 16.2-inch Liquid Retina XDR display
- Battery Life: Up to 21 hours
The MacBook Pro 16-inch is perfect for machine learning students because of the M1 Pro or M1 Max chip. All these chips are powerful and optimized for carrying out large data handling operations like machine learning and deep learning. The chips are decent for model training, and with 32 cores of GPU, the performance is commendable; moreover the Retina display provides the best quality for visualizations.
MacBooks are not quite the first choice for machine learning as they have relatively weak GPU support for CUDA, while the newer M1 Pro and M1 Max chips run a different architecture and perform very well for ML tasks. Another advantage for students with long working hours is a long battery life of the device.
2. Dell XPS 15 (2024)
Specifications:
- Processor: Intel Core i7/i9 (11th Gen)
- RAM: 16GB/32GB
- GPU: NVIDIA GeForce GTX 1650 Ti or RTX 3050
- Storage: 512GB SSD – 1TB SSD
- Display: 15.6-inch Full HD or 4K OLED display
- Battery Life: 10-12 hours
The Dell XPS 15 is a great laptop for machine learning students because of the performance and construction of the laptop. For machine learning tasks, the Intel Core i7/i9 processors are recommended together with an NVIDIA GTX or RTX GPU. The 16GB or 32GB RAM configurations make multitasking seamless while the large SSD configs allow you to store your datasets and your ML models.
Its 4K OLED display is ideal for your machine learning tasks if you require the best quality of visuals; besides, it is perfect if you work in areas that are far from the power source since it has the best battery life.
3. Asus ROG Zephyrus G14 (2024)
Specifications:
- Processor: AMD Ryzen 9 5900HS
- RAM: 16GB/32GB
- GPU: NVIDIA GeForce RTX 3060/3070
- Storage: 512GB/1TB SSD
- Display: 14 inches Full HD/144Hz or 2K/120Hz
- Battery Life: Up to 10 hours
For ML students needing a potent compact device, the Asus ROG Zephyrus G14 is a contender on this list. It has an AMD Ryzen 9 CPU and an NVIDIA GeForce RTX GPU to run complex computer learning applications such as deep learning, and training big data sets. The 16GB or 32GB RAM options along with fast SSD storage makes it very efficient for ML work to run on the laptop.
This laptop might be developed from gaming, but it is perfect for the ML student because of the performance and portability. The options for display are very pleasing and the battery power that comes with the gadget is sufficient for a day’s work.
4. Razer Blade 15 Advanced (2024)
Specifications:
- Processor: Intel Core i7/i9 (11th Gen)
- RAM: 16GB/32GB
- GPU: NVIDIA GeForce RTX 3070/3080
- Storage: 512GB/1TB SSD
- Display: 15.6-inch Full HD or 4K OLED
- Battery Life: 6-8 hours
The Razer Blade 15 Advanced is one of the best gaming laptops, which is perfect for machine learning. Its equipped with an Intel Core i7 or i9 processor and NVIDIA RTX GPU and it offers great power for deep learning and machine learning. The 16GB or 32GB of RAM makes multitasking easy and the SSD versions will give easy access to data.
The Razer Blade is ideal for visualizations and while it does not last as long as other options, it is a good choice for the ML student that needs portability.
Conclusion: Which Laptop is Better for Machine Learning
Selecting the best laptop for machine learning student depends on the following factors; cost and the type of machine learning exercises that you intend to undertake. The two laptops that I recommend for most ML students are the Apple MacBook Pro 16-inch and Dell XPS 15 because they give high performance with good battery back up for different ML tasks. If you are interested in a specific laptop for gaming with improved GPU, you can have Asus ROG Zephyrus G14 and Razer Blade 15.
Lastly, the specific model that is ideal for use in machine learning would have to be one that can provide for the specific requirements such as the processing unit, the CUDA cores, the storage, and portability. Select carefully and your laptop will be your most useful companion on the path of machine learning.