Best Laptops for Deep Learning & Machine Learning in 2023

Students who have a major in deep learning or machine learning are usually torn between two choices. Whether to buy an affordable laptop and take advantage of cloud computing services like Azure, Google Collab, and AWS, or invest money on a powerful brick. The dilemma haunts the professionals too.

Each choice has its own pros and cons, but if you are a student, investing in a laptop makes more sense. The shared cloud computing resources are usually slower than dedicated laptops and the latter would allow more hands-on experience.

Another conflict arises when you have to choose a PC or a laptop. The  PC is suitable for long-term advanced training and if overheating is one of your major concerns. But, for the students, the fact that we can take our machine to a library, classroom, or almost anywhere we want, makes laptops still a better choice. Especially, when the performance is comparable to a dedicated PC.

We have written this post with engineering students and professionals in mind looking for the best laptop for deep learning or ML projects. Each laptop we picked here has some exceptional feature that differentiates them from the rest except for a single thing: all of them have a powerful GPU in common, which essentially makes or breaks a laptop performance in deep learning.

So without further ado, let’s get started.

Best Laptop for Deep Learning & Machine Learning 2021

In the following table, we have broken down the key features of the laptops, and have to pit them against each other. The scope of your deep learning project (beginner to advanced) should be the deciding factor.

Lenovo Legion 5:

Features and Specs	Lenovo Legion 5
Dimensions HxWxD	1″x 14.3″ x 10.2″
Display:	15.6″ FHD (1920 x 1080)
Storage:	512GB NVMe SSD
RAM:	16GB DDR4 RAM
Graphics Card:	NVIDIA GeForce RTX 3050 Ti ( 4 GB)
Processor:	AMD Ryzen 7 5800H Processor
Operating System:	Windows 10 Home
Weight:	‎5.28 pounds
Battery Life:	‎Up to 8 hours with polymer 80Wh

Build Quality:

The legion lineup by Lenovo is aimed at gaming enthusiasts and that explains the bulky design and the heavy build of the laptops. 

This model has a plastic chassis and lid that is considerably thick to pass as a durable design. The portability is one of the big benefits of a laptop, but with Legion 5, you might have to compromise on it. At 5.14 lbs, the laptop is considerably heavy to lug around on the campus, but the weight is common in high-power laptops. Regrettably, the laptop is about 1-inch thick, you will have a hard time putting it inside your backpack. 

In addition to this, we get a 15.6″ FHD (1920 x 1080) Display with all the benefits of an IPS screen. The backlight keyboard is responsive and comfortable to use. 

With plenty of ports on the sides and back, Legion 5 offers a wide range of connectivity options. Plus there has been a generous number of vents around the sides to manage the heating.

Lastly, the fans are usually quiet on this laptop. However, when running deep learning algorithms, they might crank up to manage the heat, and you might hear a buzzing sound. 

How is it good for deep learning?

Deep learning projects involve tons of mathematical calculations in parallel, and the sequential-based CPU is not the best at it. GPU runs the show here.

Legion 5 comes packed with NVIDIA GeForce RTX 3050 Ti which in turn has 2,560 CUDA cores and a more powerful 80 Tensor Cores. For those who don’t know, Tensor cores are faster than CUDA cores and also more power-efficient.

Moreover, it comes with a 4 GB VRAM, which is only good for entry-level deep learning projects. As you get experienced, you can always opt for cloud-based computing on free resources by Google Colab, Kaggle, or AWS.

The 512 GB SSD storage is enough to install top deep learning software like NDeepLearningKit, Microsoft Cognitive Toolkit, Keras, ConvNetJS, Torch, Neural Designer, H20.aim, Deeplearning4j, Apache SINGA, Pytorch, and TensorFlow. However, you might run short of storage in the long run.

The 16 RAM is double the baseline 8 GB needed for entry-level deep learning. Though the processor takes the back seat in training models, Legion 5’s AMD Ryzen 7 5800H Processor is fast and powerful enough to take notice of.

For cooling, the Lenovo Legion 5 uses a combination of heatsinks, heat pipes, and fans. The laptop won’t heat to dangerous levels, but as mentioned earlier, you might hear loud fan noise.

The battery life hovers between 5-8 hours, though running intensive programs or gaming take a hit at the battery performance and reduce it to 3 hours.

Razer Blade 15 Advanced 

Features and Specs	Razer Blade 15 Advanced
Dimensions HxWxD	0.7×13.98 x 9.25  inches
Display:	15.6″ Full HD 300Hz, 100% sRGB, 4.9 mm bezel, factory calibrated
Storage:	512GB SSD (M.2 NVMe)
RAM:	16GB Dual-Channel (8GB x 2) DDR4-2933MHz
Graphics Card:	NVIDIA® GeForce RTX™ 2070 SUPER™ with Max-Q (8GB GDDR6 VRAM)
Processor:	10th Gen Intel® Core™ i7-10875H 8 Core (2.3GHz/5.1GHz)
Operating System:	Free update to Windows 11 when available.* Comes with Windows 10 Home
Weight:	4.56 pounds
Battery:	4 Hours

Build Quality:

Razer 15 looks like the Window’s alternative to a Macbook that is built primarily for gaming. The sleek form factor and powerful specs make it suitable for beginner and intermediate-level deep learning. 

In the design department, the laptop has a premium look thanks to its metal chassis, sharp edges, and slim form factor. This iteration measures 0.7-inch in thickness and weighs just above 4 lbs. Compared to Legion 5, this laptop is somewhat convenient to put in the backpack. 

There’s a balanced amount of travel and feedback, neither too mushy nor too resistant, and the keys are individually backlit with customizable RGB lighting. The included Razer Synapse software lets you change each key’s color and visual effects to create appealing patterns or useful highlights.

Razer Blade 15 is ideal for both peripherals and secondary displays. You can connect a 4K external monitor with the laptop.

For the heat problem that may arise when training complex models, Blade 15 utilizes a vapor cooling chamber. The laptop gets hotter at times, but not too hot to be unusable. However, the company has done an excellent job to keep the fan from revving up too loudly.

 Is Razer Blade 15 Good for Deep Learning?

Razer Blade 15 has gone big on the Tensor Cores, with its 288 cores for parallel computations. Compared to the Legion 5 it has fewer CUDA cores at around 2304, but as far as performance in deep learning projects is concerned, Razer Blade 15 has an advantage. Tensor Cores can perform multiple operations per clock cycle, hence you will get optimum power efficiency and speed in machine learning models.

To ensure that the performance doesn’t throttle when training models, it comes with 512GB SSD and 16 GB of RAM. Loading data to the memory is faster and the RAM is large enough to support a large data set. The GPU has its own 6 GB VRAM to further minimize the latency. Its 10th generation core i7 processor comes with 8 cores and is suitable for entry-level training.

As far as the battery life is concerned, the Razer Blade 15 can last for about 4 hours with GPU running at full performance.

Acer Predator Helios 300 

Features and Specs	Acer Predator Helios 300:
Dimensions HxWxD	0.90x x14.3×10.04 x 10 inches
Display:	15.6″ IPS LCD 144Hz, ComfyViewIn-plane Switching (IPS) Technology
Storage:	512GB SSD
RAM:	16 GB Dual-Channel (8GB x 2) DDR4 SDRAM MHz
Graphics Card:	NVIDIA® GeForce® RTX™ 3060 with  (6GB GDDR6 VRAM)
Processor:	Intel® Core™ i7-11800H, 2.40 GHz,Octa-core (8 Core™)
Operating System:	Windows 10 Home
Weight:	5.51 lbs
Battery:	3-4 hours

Build Quality:

Predator Helios 300’s build is a good mixture of aluminum and plastic. The chassis corners are sharp with gaps on the sides for proper airflow.

The weight and thickness at 5.5 lbs and 0.9-inch are almost the same we get in Legion 5. No doubt, there is considerable heft and chunkiness to these laptops. As far as portability is concerned, both these laptops lag far behind the Razer Blade 15.

Its 1920 x 1080-pixel display is colorful and bright for gaming. The vibrant colors of the screen have nothing to do with deep learning, but they enhance the aesthetic appeal of the laptop.

The connectivity would be the last of your headaches with these laptops, as there are multiple ports around the chassis. Moreover,  the keyboard support four-zone RGB lighting that can be customized with proprietary software.

Is Predator Helios 300 good for Deep learning?

Despite being a gaming laptop, this one came at the top of many deep learning benchmarkings. (it offers better speed and overall performance than Google Collab Pro).

Starting with the GPU, Acer packed the latest Nvidia GeForce RTX 3060 GPU with 6GB of VRAM in this laptop.

Like Legion 5, the Predator Helios 300 is focused more on CUDA cores. Compared to mere 28 Tensor Cores, there are 3684 CUDA cores for AI projects. One advantage with the CUDA cores you get is the accuracy in computation, but the speed and power efficiency ensured by Tensor cores are more desirable in deep learning projects.

The Intel Core i7-10750H processor with 16GB of RAM and 512 GB SSD storage makes it closer to the Razer Blade 15 in hardware except for the GPU. Compared head to head, the latter can beat it in long-term model training due to its Tensor Cores.

However, Acer does have a slick trick up its sleeves. The purposefully engineered AeroBlade 3D Fans cool the system quite efficiently.

Another advantage is that of battery life. Unlike the 3-4 hours mark we get in other high-end laptops, Acer hits the 6-hours mark quite easily even when running the GPU at full capacity.

Pro Tip: In some parts of the world,  Nvidea is offering access to its Deep Learning Institute (DLI) Workshops with every purchase of Predator Helios 300. (Worth More than $500).

Acer Nitro 5 AN517-54-77KG Gaming Laptop ( best budget laptop)

Features and Specs	Acer Nitro 5 AN517-54-77KG
Dimensions HxWxD	0.98 x 15.09 x 11 inches
Display:	17.3″ FHD 144Hz IPS Display
Storage:	1TB NVMe SSD
RAM:	16GB DDR4
Graphics Card:	NVIDIA GeForce RTX 3050 Ti (4GB dedicated GDDR6 VRAM)
Processor:	Intel Core i7-11800H Processor – up to 4.6GHz, 8 cores, 16 threads, 24MB Intel Smart Cache
Operating System:	‎Windows 10 Home
Weight:	4.85 lb
Battery:	‎ Approximately 5 hours

Build Quality:

The Acer Nitro 5 is a plastic-built gaming laptop and slightly lightweight laptop on our list, weighing 4.85 pounds compared to Acer Predator Helios 300 5.5 pounds at a more affordable price tag. 

Like the laptops we discussed earlier, this one also supports a 17.3-inch, 1080p, 144Hz refresh display, which is still sharp and clear. However, there is considerably more bezel around the screen.

It is equipped with both USB Type C and USB type-A ports, which allows you to connect older peripherals like mice or keyboards as well as future technology like VR headsets. The keyboard is illuminated by RGB and is quite smooth and convenient to use. (a mere 1.6mm travel distance)

Why it is good for deep learning?

Acer Nitro 5 is equipped with an Intel Core i7 processor that is perfect for heavy, advanced gaming applications as well as for machine learning projects.  It has a DDR3 Ram of 16GB, which is more than adequate for entry-level training and can be updated to 32GB capacity.

However, the real performer of the show is the graphic card.

Acer Nitro 5 also features the NVIDIA GeForce RTX 3050Ti GPU with 4GB DDR6 VRAM, enabling it to handle complex deep learning algorithms with ease. Acer Predator Helios 300 has a very similar setup but includes an NVIDIA GeForce RTX 3060Ti GPU with 6GB DDR6 VRAM instead and costs about $100 more than Acer Nitro 5.

We get a combination of 2560 CUDA cores and 80 Tensor Cores. The latter is more important as it helps in deep learning training and inferencing. You can also train advanced neural networks with tensor cores.

Acer cool boost technology keeps the temperature to a minimum. It increases the fan speed by 10% and CPU/GPU cooling by 9% compared to auto mode. The high-end vapor chamber found in R4 is absent in the R6 model so the latter’s cooling system takes a hit when running sustained GPU-intensive tasks.

Despite the Acer claim that the battery is built to last for 7 days, when put the test, it was reduced to merely 5 hours. That is when you are not running demanding programs. However, we have found it better than the Razer Blade 15 and Lenovo Legion 5.

Alienware M15 R6 Gaming Laptop

Features and Specs	Alienware M15 R6 Gaming Laptop
Dimensions( HxWxD)	0.89x 10.73 x 14.02 x inches
Display:	15.6-inch FHD 360Hz Display
Storage:	1TB SSD
RAM:	32GB DDR4 RAM
Graphics Card:	NVIDIA® GeForce RTX™ 3080 (8GB VRAM) GDDR6
Processor:	Intel Core i7-11800H
Operating System:	‎Windows 11 Home
Weight:	‎5.93 pounds
Battery:	‎ 6 hours

Build Quality:

Alienware M15 has a plastic construction and a considerable heft. The laptop weighs 5.93 pounds, but Dell has managed to keep the thickness under 9-inch.

Full HD (1080p) display is what we expect in mid-range laptops, and Alienware M15 is no different. The screen quality is both crisp and bright. The viewing angles are great too.

Dell has been generous in the use of RGB lighting. The whole keyboard along with the Alienware head logo and the back I/O strip is fully illuminated. This surely has the looks and feel of a gaming laptop.

As far as the connectivity is concerned, the laptop doesn’t disappoint you in any way. Left to right there are plenty of ports to cater to all kinds of needs.

Is Alienware M15 R6 good for deep learning?

When it comes to deep learning and ML, the powerful GPU (RTX 3080) is the backbone of this laptop. With 2nd generation Tensor Cores, approx. 6144 CUDA cores and 8GB DDR6 VRAM, RTX 3080 is a beast that can chew down complex training modes.

You can overwork the laptop without having to deal with the heat problem. Nevertheless, the sustained 100% deep learning load on the GPU might harm the laptop in the long run. The laptop would do perfectly fine if you are planning to generate a bunch of epochs overnight.

There is a thoughtfully designed air cooling system in place but it generates a lot of noise. Unfortunately, you would have to live with the winning noise of the fan when running the laptop at full capacity.

You are well-served with the 32 GB RAM and 1TB SSD storage. A large memory ensures that your model is capable to take a large data set, which in turn enhances the overall speed of the train. The same goes for the  M.2 SSD, it’s as fast as you would want it to be for deep learning.

Lastly, the laptop lasts for approximately 6 hours on a full charge, that is when you are not involved in intensive tasks. The performance during high-end gaming is quite humble at 4 hours. For long-term training, you might have to neer the power outlet all the time, which defeats the purpose of a dedicated laptop for machine learning.

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FAQs:

1. MacBook Pro For Machine Learning Or Data Science? 

MacBooks are not recommended for deep learning as they don’t support Nvidia graphic cards, and have a poor cooling system. Buying an expensive Macbook, just to get a throttled performance due to overheating doesn’t make sense.

However, if you are planning to use cloud services like AWS, Google Colab, Sagemaker,  or Kaggle, and buying the Macbook just to access them, you can go ahead.

In our research, we have found several data scientists who train their Ai models on a Macbook. Here is an interesting finding. When compared for several deep learning tasks, Macbook Air (M1) came at the top when compared with Macbook Pro 13-inch and Macbook Pro 16-inch. The heating problem remains.

2. Laptop Vs Desktop For Machine Learning?

Most professionals would recommend you not to spend your scarce resources on personal hardware like a laptop or desktop if you intend to buy it for purely machine and deep learning. Cloud services offer you much better prices and high specs.

But if you still want to know which setup is better? The short answer is desktop. The first and foremost reason for it is the raw power it has to overcome the demands of deep learning. Second, and more important, is that you can easily tackle the heating problem on a PC.

However, if you are an undergrad or master’s student who just wants to complete the beginner data science track, a laptop with decent specs (i7 Processor, Cuda-enabled GPU, 16 GB RAM, 500 GB SSD)is more suitable mainly because of its portability.

3. Are more CPU Cores better for Deep Learning projects?

Cores are the number of individual CPUs in a chip. Unlike thread, they are actual hardware components.

As most deep learning tasks require parallel computations, only if the CPU has more cores, your system can assign maximum cores for parallel tasks. More cores will help your system to process complex computational tasks like the Random Forest algorithm better.

For deep learning, your system’s CPU must have 4 cores as a base minimum. The more the better. They can take on bigger problems and train models faster than lower-core CPUs with similar specs.

4. Machine Learning Vs Deep Learning?

Both are subfields of Artificial Intelligence (AI) that use multiple layers to detect patterns in data. Deep learning is a further subfield of machine learning which uses multi-layered neural networks to analyze data and provide insight into what they’ve learned.

The line that divides machine learning and deep learning is rather blurred but is still there. Deep learning algorithms require less human input, are more advanced, and deal with complex data. It requires more GPU power and takes significantly more time to train.

5. Should I prefer a better GPU or CPU For Deep Learning?

CPUs are better at processing single, more complex computations sequentially, while GPUs are better at performing numerous but simpler tasks simultaneously. 

If you are just beginning with Deep learning then definitely go with a better CPU. It would be easy to train and deploy models using CPU rather than GPUs which have higher computational power but are expensive too.  

However, with large-scale neural networks, a better GPU offers better efficiency and performance. The presence of a large number of cores makes them better suited to compute multiple parallel processes. Also, their memory bandwidth (they have dedicated VRAM) enabled them to handle tons of image data.

6. Does Ram/SSD Storage Help With Deep Learning? 

As far as the system RAM is equal to that of the largest GPU on your system, you are good to go. The system memory doesn’t help in deep learning, but if it falls below the VRAM of the GPU, there is a great chance of a bottleneck situation.  With better RAM, you can load more data at a time and there would be little need to access storage, thus a fast transfer of data.

The same goes for SSD. It helps in the fast retrieving of data but the overall contribution to deep learning is marginal with respect to other specs like a CPU  or GPU.

7. Cloud Services vs Personal Laptop/Pc For Machine or Deep Learning? 

Cloud services by Amazon (AWS), Google (GC or Google Colab), Microsoft (Azure) and Kaggle offers better resources at a competitive price to train deep learning algorithm. 

The investment on these platforms makes sense as you get access to the finest computational resources like the latest GPUs (RTX 2080 Ti, RTX 2080 SUPER, RTX 2070 SUPER) and powerful processors, to get through your master’s or Ph.D. degree. You can access them from any $500 laptop. 

To build your own PC or laptop for deep learning is an overwhelming project, and if you need it for a limited time, the investment it demands puts a hole in your pocket.