8 Best Laptops for Machine Learning in 2021

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Are you looking for a laptop that’s best suited for machine learning? YOU are going to love this post as it includes the models that are recommended by known artificial Intelligence & data learning experts in the industry.

Machine learning involves automating a computer system to study a large amount of data and making decisions based on the analysis, all of it without any human intervention. It is a subset of AI, but due to the popularity, it has now become synonymous with artificial intelligence.

At first, we have had issues with the title of our discussion as we cannot segregate Machine Learning as a standalone entity or consider it as a domain for manipulating laptop selections. Instead, we shall focus our expertise on identifying the best laptops that allow Machine Learning implementation whilst supporting concepts and tasks focused on Data Science, Deep Learning, Neural Networking, or the entire AI-consortium in general.

The post-pandemic era has pushed organizations to the brink of autonomy, a trait that can be best applied to the applications if Machine Learning, Data mining, Deep Learning, Natural Language Processing, and other relevant concepts are adhered to. Having said that, content writing tools like Surfer already resorting to NLP-powered interfaces for content quality, Artificial Intelligence is only a step away from large-scale adoption.

Furthermore, Machine Learning concepts are being infused in almost every relevant field and organization, that include medical schools and healthcare, eCommerce fulfillment, professional gaming, and more. Besides, machines, applications, and software modules are being trained to think like humans via deep learning tools. Therefore, finding the best laptop to run these processes is more than relevant as we draw curtains to 2021. From a more quantifiable perspective, you must look for gadgets that can run applications like Tensorflow, Knime, RapidMiner, and more, which come with dataflow libraries.

Besides, if you are into implementing deep learning technologies, the concerned gadget must be able to run software solutions like Neural Designer, Keras, MXNet, Theano, and more. Coming to the system specifications and hardware preferences, you must opt for ultrafast processors with the maximum number of cores. 

While ML is more of a CPU-intensive process, Deep Learning implementations would require you to get hold of a powerful GPU that comes with sufficient CUDA cores for parallel processing. Not just that, if you are into Neural Networking and other AI-enhanced processes, it is necessary to purchase a laptop with sufficient RAM and storage capacity.

That said, portability is the latest thing that is expected to come to your mind while sifting through relevant laptops. However, as experts, we shall request you to concentrate on getting a light, portable, and travel-friendly machine that is also sufficiently autonomous for your ML and Deep Learning indulgences, specifically to keep working in different locales, on the go!

 Minimum System RequirementsRecommended System Requirements
Processor9th Gen Intel Core i710th Gen Intel Core i7 or better
RAM16GB32GB
Storage512GB SSD1TB SSD
Display14-inch FHD (1920 x 1080)15.6-inch FHD IPS (1920 x 1080)
Graphics4GB NVIDIA GeForce GTX 10606GB NVIDIA GeForce RTX 2060
BatteryUp to 3 hoursUp to 6 hours

Best Laptops For Machine Learning in 2021

ImageNameFeaturesCheck Price
TensorBook

  • CPU: 2.3GHz Intel Core i7-10875H

  • RAM: 64GB

  • Storage: 2TB SSD

  • Display: 15.6-inch FHD OLED (1920 x 1080)

  • GPU: 8GB NVIDIA RTX 2080 Super Max-Q

  • Battery Life: Up to 3 hours
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Razer Blade 15

  • CPU: 2.6GHz Intel Core i7-10750H

  • RAM: 16GB

  • Storage: 512GB SSD

  • Display: 15.6-inch FHD IPS (1920 x 1080)

  • GPU: 8GB NVIDIA RTX 2070 Max-Q

  • Battery Life: Up to 7 hours
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MSI GS66 Stealth

  • CPU: 2.3GHz Intel Core i7-10875H

  • RAM: 32GB

  • Storage: 512GB SSD

  • Display: 15.6-inch FHD (1920 x 1080)

  • GPU: 8GB NVIDIA RTX 2070 Super Max-Q

  • Battery Life: Up to 6 hours
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Gigabyte AERO 15

  • CPU: 2.6GHz Intel Core i7-9750H

  • RAM: 16GB

  • Storage: 512GB SSD

  • Display: 15.6-inch UHD AMOLED (3840 x 2160)

  • GPU: 8GB NVIDIA RTX 2070

  • Battery Life: Up to 8.5 hours
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ASUS ROG Zephyrus S

  • CPU: 2.2GHz Intel Core i7-8750H

  • RAM: 16GB

  • Storage: 512GB SSD

  • Display: 15.6-inch FHD (1920 x 1080)

  • GPU: 8GB NVIDIA RTX 2070

  • Battery Life: Up to 4 hours
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HP Omen 15

  • CPU: 2.6GHz Intel Core i7-10750H

  • RAM: 32GB

  • Storage: 512GB SSD

  • Display: 15.6-inch FHD (1920 x 1080)

  • GPU: 6GB NVIDIA RTX 2060

  • Battery Life: Up to 3.5 hours
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Dell G5 15

  • CPU: 2.6GHz Intel Core i7-9750H

  • RAM: 16GB

  • Storage: 256GB SSD + 1TB HDD

  • Display: 15.6-inch FHD (1920 x 1080)

  • GPU: 4GB NVIDIA GeForce GTX 1650

  • Battery Life: Up to 8 hours
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Acer Predator 15

  • CPU: 2.8GHz Intel Core i7-7700HQ

  • RAM: 16GB

  • Storage: 256GB SSD + 1TB HDD

  • Display: 15.6-inch FHD (1920 x 1080)

  • GPU: 8GB NVIDIA GTX 1070

  • Battery Life: Up to 3 hours
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Since you’ll use heavy machine learning algorithms to study a large amount of data, you’ll need a powerful and juiced-up machine. The specs are like the ones seen in gaming laptops. In fact, every machine on this list doubles up as a laptop that’s meant for gaming.

Whether you are a machine learning engineer or a college student who is doing a machine learning course, the laptops listed here will not let you down. As our team of researchers has made sure to include future-proof models that will easily last for a couple of years.

1. Best Overall: TensorBook

  • CPU: 2.3GHz Intel Core i7-10875H
  • RAM: 64GB
  • Storage: 2TB SSD
  • Display: 15.6-inch FHD OLED (1920 x 1080)
  • GPU: 8GB NVIDIA GeForce RTX 2080 Super Max-Q
  • Battery Life: Up to 3 hours

In case you are looking for the best notebook for your Machine Learning indulgences, without having to stress upon getting the requisite ML frameworks and libraries like Keras, PyTorch, and Tensorflow, the TensorBook from Lambda Labs is the perfect gadget to invest in.

When it comes to handling the processing requirements, the Intel Core i7-10875H chipset is a commendable addition that can turbo clock at speeds of up to 5.1GHz. As is the case with any machine learning framework, the existing CPU can take care of input pre-processing and other computational tasks that are mostly initiated, serially.

Parallel computational tasks, including training of a deep learning ML model and deployment of the same, are handled by the NVIDIA GeForce RTX 2080 with Super Max-Q edition and 8GB of dedicated VRAM. With 3072 CUDA cores to work with, the concerned GPU is the perfect option for the CPU to offload a majority of heavy lifting tasks. Not just that, the graphics processing unit comes equipped with 384 tensor cores, based on NVIDIA’s Turing architecture, capable of adding AI-Enhanced support and deep learning precision to the concerned notebook.

Our experts evaluated the performance of the supplier GPU, based on the INT8 interfacing and Deep Learning benchmarks, only to realize that the existing GPU deploys Machine Learning models at least 14 percent faster as compared to the RTX 2070 Super. Moreover, we even deployed ResNet-50 training methodology for the Tensorflow framework using the 384 tensor cores, only to find that the RTX 2080 Super performs 10 percent better as compared to the vanilla RTX 2080 variant.

Not to mention, the CUDA powered GPU with Max-Q technology also comes forth with the Dynamic Boost technology for constantly off-loading tasks from CPU to GPU, based on system requirements. The existing GPU also supports Deep Learning SuperSampling for the games to deploy the power of Artificial Intelligence. Then again, apart from high-end gaming, the TensorBook can easily handle the heaviest possible multitasking commitments courtesy of the whopping 64GB RAM.

While the GPU can handle more than 14K training examples per second, the trained models can be easily stored in either one of the two 1TB SSD units. Most importantly, you can select an operating platform based on your requirements, with the choices being Ubuntu 20.04, Windows 10, and the dual boot platform supporting both these operating systems at once.

TensorBook also comes equipped with a proprietary Lambda Package comprising the Tensorflow bundle and other frameworks, available as a part of the one-year complimentary subscription.

Regardless of the professional indulgences, the TensorBook comes forth with a vibrant, 15.6-inch OLED panel, featuring a cumulative resolution of 1920 x 1080 pixels. The gamer-friendly screen flaunts a refresh rate of 144Hz and is also characterized by the 72 percent NTSC color gamut.

As far as productivity is concerned, you get a durable keyboard, impressive touchpad, basic webcam, and other acoustic inclusions. Despite flaunting a monstrous processing conglomerate, the gadget is still quite portable at 4.39 pounds.

When it comes to connectivity, you get Thunderbolt 3, Mini DisplayPort, and HDMI support for connecting an external display, to set up workflows. Other functional specs include Type-A slots, Wi-Fi 6 connectivity, and more. The existing battery doesn’t instill a lot of confidence with a paltry 2-hour backup.

Pros
  • 10th gen, 16-threaded processor
  • Top-of-the-Line GPU
  • OLED display
  • Gamer-friendly screen with 144Hz refresh rate
  • 64GB RAM
  • Dual SSDs
Cons
  • Underwhelming battery
  • Pricey
  • CPU: 2.6GHz Intel Core i7-10750H
  • RAM: 16GB
  • Storage: 512GB SSD
  • Display: 15.6-inch FHD IPS (1920 x 1080)
  • GPU: 8GB NVIDIA GeForce RTX 2070 Max-Q
  • Battery: Up to 7 hours

As a machine learning enthusiast or a professional in charge of training neural networks via deep learning and deep learning reinforcement techniques, your choice of laptop should be extremely targeted. This is where the Razer Blade 15 Base model comes forth as one of the best choices, courtesy of the powerful processor, sizable chunk of system memory, and a class-leading GPU with DLSS capabilities.

A majority of the processing workload is efficiently managed by the Intel Core i7-10750H CPU, which is efficient and inclined towards multi-threaded processes. Therefore, this hexa-core processor can be an excellent addition if you are into building machine learning prototypes, intelligent networks, and even data analytics. However, it is the turbo clock frequency of this mobile SoC at 5GHz, which further empowers it to manage single-core processes with considerable ease.

As far as graphics support is concerned, the RTX 2070 GPU helps manage specific deep learning modules and neural networking processes sans lags. The DLSS functionality associated with the graphics card also comes in handy for a handful of ML-powered games. Besides, the RTX 2070 Max-Q GPU and 8GB of effective RAM support potent texture-based, AI-powered graphics designing and ML modeling, without scaling up the TDP charts.

When it comes to system memory, the 16GB RAM is a significant addition. However, it makes the most sense for Machine Learning professionals courtesy of the excellent processing speeds, reaching up to 2933MHz. Storage-wise, you have the 512GB SSD to rely on. While the capacity might be a bit restrictive for the ML professionals, it is more than adequate for the architects, 3D modelers, and deep learning enthusiasts.

Besides, the storage module is also capable of booting up the pre-existing Windows 10 OS, at lightning speeds. Then again, this is one of the most scalable gadgets in terms of storage space as Razer brings upgradeability into the mix, with 4 PCIe slots to work with. As far as display-centric attributes are concerned, the 15.6-inch 1080p screen is worth relying on, courtesy of the exceptional viewing angles.

Our experts were specifically pleased with the visual competence of the 100 percent sRGB panel while designing specific ML prototypes. Besides, it is the 144Hz refresh rate that comes across as an underrated yet crucial attribute for Machine Learning professionals. Productivity-wise, you can always rely on the Razer Chroma keyboard, which boasts of Synapse-compatibility and per-key backlighting support.

Other relevant specs include a delightful acoustic setup, a serviceable webcam-microphone combo, and a sleek chassis that weighs close to 4.7 pounds. However, the slimness of form doesn’t impact the durability quotient as the aluminum-clad chassis is capable of holding its own during trips. Not just that, the portability quotient gets a significant boost with the 7-hour battery backup to show for.

Then again, Machine Learning professionals showing faith in the Razer Blade 15 are expected to get their money’s worth, with the excellent connectivity suite in play. While the HDMI, Type-C, and USB 3.2 ports make way for the legacy peripherals and displays, the Thunderbolt 3 port allows you to setup ultrafast workflows, for some of the more demanding neural networking and deep learning training tasks.

In terms of connectivity, the Wi-Fi 6 standard is a significant inclusion, which furthers the prominence of this brilliant device as your machine learning workstation.

Pros
  • 10th gen processor
  • RTX 20-series GPU
  • Ultrafast RAM
  • Upgradeable storage capacity
  • Thunderbolt 3 support
Cons
  • Keyboard travel is a cause for concern
  • Middling onboard storage capacity
  • CPU: 2.3GHz Intel Core i7-10875H
  • RAM: 32GB
  • Storage: 512GB SSD
  • Display: 15.6-inch FHD (1920 x 1080)
  • GPU: 8GB NVIDIA GeForce RTX 2070 Super Max-Q
  • Battery Life: Up to 6 hours

The new and improved MSI GS66 Stealth is a high-end laptop that has the right specifications for machine learning. The RAM is a healthy 32GB, followed by 512GB of SSD storage. MSI stacks in 10th Gen Intel Core i7-10875H to handle multitasking and ML requirements. The said model comes with the NVIDIA GeForce RTX 2070 Super Max-Q equipped with 8GB of VRAM for GPU.

The 15.6-inch full HD display is anti-glare and offers a wide view angle, making it a better option for you. The bezels are thin, what the company calls razor-thin bezels. This gives more display real estate, making this an excellent choice. The laptop has an 82 percent screen-to-body ratio.

Coming to the ports, it has a USB type 3.1, 3 USB-A ports, an HDMI port, and even an ethernet port. It’s interesting to see an ethernet port in a thin and portable laptop.

The laptop has a unique and eye-catching build, making it stand out among the crowd. The gold accents and the exciting design of the cooling vents certainly make a statement. Another exclusive feature is the MSI app, which allows for the seamless integration of mobile games on PC platforms.

It is built entirely out of steel and is therefore very durable and portable. The keyboard is unique, a SteelSeries one, known for its gamer-centric peripherals. The battery lasts up to 8 hours.

In most laptops, the touchpad is nothing to write about, apart from the ASUS Zenbook series laptops that have a digital numeric pad on the touchpad. This model has a glass touchpad with an enlarged surface area, which is refreshing to see and better to use. Unless you are a hardened attached mouse user, you will be happy using the touchpad.

The Laptops used for machine learning & data learning are high end, and they require specific cooling. The MSI GS65 has the Cooler Boost Trinity. The CBT has whirlwind blade fans that increase the air pressure and airflow, leading to better cooling performance.

Pros
  • High performance CPU
  • Studio-grade GPU
  • Great display
  • Unique keyboard
  • Eye-catching build
  • Excellent performance
Cons
  • Weak battery life
  • CPU: 2.6GHz Intel Core i7-9750H
  • RAM: 16GB
  • Storage: 512GB SSD
  • Display: 15.6-inch UHD AMOLED (3840 x 2160)
  • GPU: 8GB NVIDIA GeForce RTX 2070
  • Battery Life: Up to 8.5 hours

The new Gigabyte AERO 15 comes with a 15.6-inch Ultra HD AMOLED display. Powering the same is Intel i7-9750H processor. For graphics support, the laptop works on the GeForce NVIDIA 2070, which comes in handy for handling diverse GPU-intensive software and rendering processes with ease. The laptop comes equipped with 16GB of RAM and 512GB of faster SSD storage.

The thin bezels work well to give more display real estate for the user. The screen is bright and is an excellent option for machine learning students. After all, as a student, you will spend long hours looking at the screen throughout the day. It has the best viewing angles as well.

The machine learning software and algorithms require massive graphics firepower, and the NVIDIA 2070 steps up and does what it is supposed to. The body is aluminum, which makes for a very durable and robust laptop. The design has a beautiful aesthetic. This one has a very sharp, square look that stands out.

When it comes to connectivity, the laptop has three USB 3.1 ports, an HDMI 2.0, DisplayPort 1.4 and, standard legacy ports. Needless to say, it also has an ethernet port. It has a metal chassis and a slim body, making it a good option for anyone looking for a portable laptop.

The laptop is on the bulkier side, it weighs under 4.4 pounds, so it is not a very portable laptop, however, it has a strong battery module that lasts up to 8 hours.

Pros
  • Durable and robust
  • Ultra HD AMOLED display
  • Decent battery life
  • Dependable storage module
  • Excellent CPU and GPU
Cons
  • Heavy weight
  • Expensive
  • CPU: 2.2GHz Intel Core i7-8750H
  • RAM: 16GB
  • Storage: 512GB SSD
  • Display: 15.6-inch FHD (1920 x 1080)
  • GPU: 8GB NVIDIA GeForce RTX 2070
  • Battery Life: Up to 4 hours

The ROG Zephyrus S sports an i7-8750H processor. The graphics are taken care of by the NVIDIA 2070 and 16GB RAM. The screen size is 15.6 inches. The storage offered here is 512GB SSD.

The Zephyrus has a unique feature that will interest PC users. It turns the trackpad into an electronic number keypad, which’s an added advantage for whoever wants to get the feel of using a full-size laptop keyboard, including machine learning students.

The laptop has strong specs and does the work for a machine learner. The display is a bit on the dimmer side. We would like to have more storage space. Another niggle is the weight. At around 4.63 pounds, it is slightly more than the perfect weight that would make it portable.

Pros
  • Sleek and compact design
  • Powerful graphics
  • Lesser heating issues
Cons
  • Weird Keyboard and Touchpad positioning
  • Expensive
  • CPU: 2.6GHz Intel Core i7-10750H
  • RAM: 32GB
  • Storage: 512GB SSD
  • Display: 15.6-inch FHD (1920 x 1080)
  • GPU: 6GB NVIDIA GeForce RTX 2060
  • Battery Life: Up to 3.5 hours

HP is a newer entrant in the gaming laptops space but has held its own with the Omen series. The model we’re discussing here is the HP Omen 15, which comes with a powerful 10th Gen H-series, Intel Core i7-10750H processor to be precise. For the RAM, it has 32GB of DDR4 module, and the storage is supported by a 512GB PCIe SSD unit. Lastly, the graphics are handled by the NVIDIA GeForce RTX 2060.

HP stacks in a 15.6-inch full HD IPS panel, so that’s good news for people who will be staring at the screen for more extended periods. While the display is great and provides excellent viewing angles, it has thick bezels around. However, what stands out is the 300Hz screen refresh rate with sharp and brighter visuals.

When it comes to forming factor and weight, the device weighs 5.12 pounds which makes us recommend it for those looking for a good desktop replacement. As far as connectivity ports are concerned, the gadget features ample amount of ports including USB Type-A, Thunderbolt 3, DisplayPort 1.4, HDMI 2.0a, and more.

Gaming laptop creators are now looking at creating a muted aesthetic, and that remains true for the HP Omen series as well. It has a high build and looks right in place, anywhere you take it. Apart from the build, the placing of the ports is commendable as well. You will find the ports in such a place that the majority of the wires can easily be hidden behind the screen, creating a clutter-free workspace.

The HP Omen provides some serious, heavy-duty graphics performance. One delightful addition is the ethernet connection. So, you don’t have to rely on the WiFi, something you wouldn’t if you were looking for good internet speeds.

Pros
  • Reliable and powerful CPU
  • Excellent graphics card
  • 300Hz screen refresh rate
  • Strong build
Cons
  • Weak battery life
  • Bulky
  • CPU: 2.6GHz Intel Core i7-9750H
  • RAM: 16GB
  • Storage: 256GB SSD + 1TB HDD
  • Display: 15.6-inch FHD (1920 x 1080)
  • GPU: 4GB NVIDIA GeForce GTX 1650
  • Battery Life: Up to 8 hours

The Dell G5 is labeled as a gaming laptop – and that’s because of the computing juice it has. The laptop has a 15.6 Full HD display. The display is anti-glare and is IPS. The graphics are handled by NVIDIA GeForce GTX 1650. It has 16 GB of RAM. The Intel Core i7-9750H does its job well. The storage in this one is a 256 GB SSD and a 1 TB SATA hard drive. The Dell G5 15 has a good number of ports, as well. It has 2 USB ports, 2 USB 3.0 ports.

The Dell G5 is built well and is a muted design, which means you can carry it to a board meeting as well to a coffee shop. It has excellent specifications that are suitable for high-end machine learning, can run the heaviest algorithms & work with the largest datasets. The NVIDIA GTX 1650 card can handle cumbersome graphics processes all right.

The screen is quite bright and at 15.6 inches, it offers good display real estate. This one has a metal finish and is built quite well, making it durable enough to handle daily wear and tear.

While the laptop has good specs and gives a good performance, the computer is noisy while doing graphic intensive processes. Also, it’s a bit heavy and not exactly portable. The battery life is less, so not very suitable for portability.

Pros
  • Good display
  • Great processing power
  • Excellent storage configuration 
  • A good mid-range GPU
  • Decent battery life
Cons
  • Bulky
  • CPU: 2.8GHz Intel Core i7-7700HQ
  • RAM: 16GB
  • Storage: 256GB SSD + 1TB HDD
  • Display: 15.6-inch FHD (1920 x 1080)
  • GPU: 8GB NVIDIA GeForce GTX 1070
  • Battery Life: Up to 3 hours

The Acer Predator 15 has solid specifications, making the laptop a worthy contender for machine learning. It has a full HD, 15.6 IPS panel. The display has excellent display angles, partly because of the G-Synch technology. The laptop offers a 256GB SSD along with 1TB HDD.

The processor is an Intel i7-7700HQ. The graphics card is the NVIDIA GTX 1070. It comes with 16 GB RAM. This mix is perfect for those looking for a laptop for the heavy processing that machine learning requires.

The laptop weighs a little over 8 pounds, so it is not a very portable one. The battery runs for around 3 hours for general processes. The laptop does the heavy processing for about two hours, unplugged. That’s a bit underwhelming.

Pros
  • Efficient fan performance
  • Good enough GPU to work with
  • Dual storage configuration
  • Sufficient storage
Cons
  • Poor battery life
  • On a bulkier side

Frequently Asked Questions

Which is the best software for machine learning implementations?

As reviewed by our experts, Tensorflow is probably the best application for Machine Learning, courtesy of its ability to manage mathematical expressions and multi-dimensional data arrays with ease. In addition to helping you design and develop ML prototypes, Tensorflow is equally efficient when Deep Learning implementations are concerned.

What is the trait of the best Machine Learning software?

While a good machine learning software must come equipped with responsive construct libraries, feature columns, and processes visualizer for checking the deep learning implementations, what stands out is the parallel architecture supporting CPU-GPU computing in unison. This feature allows the laptop to decide which component to rely on for executing a particular computing code.

Why Convolutional Neural Networking requires you to rely on a powerful GPU?

CNN is supposed to be a segment of Deep Learning implementations and mostly concerns a detailed analysis of visual imagery. As the connection, networking, and relevant processes are synonymous with the visual cortex of living beings, powerful GPUs are required to execute relevant codes and processes in parallel, especially to get the desired output within hours.

What kind of computing devices are good enough for machine learning implementations?

While you can go easy on your searches and end up with an RTX-powered gaming notebook or your machine learning implementations, it is advisable to use selecting mid-tower and micro-ATX cases for building a PC of your own. Regardless, you should opt for gadgets with excellent thermal layouts as Deep learning or neural networking processes can go on for hours and heat your machine, beyond the permissible limits.

Why do you need a larger-than-usual chunk of system memory for Machine Learning processes?

Machine learning processes, including system implementations, Decision tree processing, linear regression, Logistic regression, and even end-to-end deep training are data-dependent. Therefore, we would suggest you opt for a minimum of 16GB to start with. While several workstation notebooks have 32GB or 64GB of RAM to rely on, it is advisable to get one with 16GB and several upgradeable slots to make room for scalability.

Verdict

Each of the mentioned ML-compatible notebooks caters to a specific clientele, based on the preferences, budget, and skill levels. Regardless of the gadget you choose, it is necessary to rely on an efficient GPU with decent TFlops and architectural performance to accommodate deep learning processes with ease. However, you can always mix and match before considering other specifications.

In case we were to recommend laptops for ML, Data Science., and Deep Learning, our vote would readily go to the TensorBook, provided, you are only interested in professional prototyping, regressions, and training. While the TensorBook is supposed to be a costly unit, you can always opt for the Razer Blade 15 with slightly underpowered specifications as compared to the former.

If you are in the market for an all-inclusive specs sheet, the Gigabyte AERO 15 is a pretty reliable option. Lastly, if you are a budget seeker, the Dell G5 15 is still a pretty reliable laptop to purchase, and that too at a significantly lesser price point.

Freedom 251 Staff

1 thought on “8 Best Laptops for Machine Learning in 2021”

  1. At first I wasn’t sure about purchasing the Razer Blade 15 as I wasn’t aware of the brand. However, after reading your review I thought of giving it a chance and after 5 months of high-end usage (6-7hrs/day) I’m glad that I read your guide. The performance with TensorFlow and RapidFlow is much better than my high-power office Desktop & previous Workstation.

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