Best Laptops for Data Scientists (including AI & ML)

[@JustJoshTech]

👉 Best Laptops for Data Science: www.justjosh.tech/recommendations/Best-Data-Science-Laptops 🍎 - Apple MacBook Air 15: bhpho.to/3XUuise - Apple MacBook Pro 14 M4 Pro: geni.us/xSC3S - Apple MacBook Pro 16 M4 Max (14C): geni.us/t1YhrjD - Apple MacBook Pro 16 M4 Max (16C): geni.us/CsQrXNB 🪟 - Lenovo Yoga 7i 15 Aura Edition: geni.us/sQUv - Asus ProArt P16: geni.us/wqlnPaN - Lenovo Yoga Pro 9i 16: geni.us/n5UqzGi

[@wgm247]

This type of video demonstrates exactly why the justjosh channel is on another level to other tech/laptop channels on youtube

[@da_volkov]

Alex Ziskind's channel is good too!

[@Äpple-pie-5k]

Except he forgot to mention aspect ratio. Verticality is so key for a data-cruncher laptop.

[@smohan123]

Persona driven buying advice is really smart. Super well done

[@ArtificialDetour]

@@smohan123 Watching someone, creating an opinion on people and trusting some ppl ore then others is not 'persona driven buying advice'. Its only problematic if you just buy what he tells you without thinking yourself about it, which nobody here claimed as far as I am aware.

[@carlosalbertomunozgiron6089]

🤣🤣🤣🤣🤣 debatable

[@Mayur7Garg]

I am a professional Data Scientist and here are the things that I prefer in a laptop: Minimum requirements: 15+ inches and 16GB RAM Preferred: 32 GB RAM Case dependent: - If you use something like Excel (or other Office Suite) or in memory analytical libraries like pandas a lot, prefer laptops with a high single core performance. Also, choose the RAM based on how much data you process at once. - If you perform distributed analysis or build CPU based models (like scikit-learn), prefer high multi-core performance and high thread count. - For deep learning models for tabular data, I would prefer faster GPUs over VRAM. But depends on the scale of the data. - For deep learning models for images or LLMs, I would prefer GPUs with high VRAM over raw GPU performance. My suggestion would be 8GB+. You could get a lot more done with a higher batch size. - A good keyboard is good to have but should not be the main buying decision imo unless you are a super Excel user or something. This is since most programmers, including myself, spend most of the time reading code and data and a lot less typing. - If you use a laptop for professional use, most likely you would be using a desk style setup. In that case, a long battery life and a nice trackpad is nice to have but should not be the focus. In a desk setup, it is better to plug in the laptop and use a mouse. - For deep learning models, I prefer Nvidia GPUs since I feel like it is more supported and easier to set up. - Screens are subjective. Choose the screen type based on your budget and how likely you are to use the laptop for other stuff (gaming, Netflix, YT). Choosing the screen specifically for coding is a bit overrated imo. Bonus: - If you use Excel a lot or any IDE (VS Code, PyCharm, etc.) a lot, buy a wide screen monitor. It is a game changer for development. These are just my opinions and preferences.

[@Crazy_CJ_]

Yeah but for machine learning we need vram and cuda cores but I am still a newbie

[@higochumbo8932]

@@Crazy_CJ_ With wide screen you mean ultrawide?

[@kots9718]

as a fellow programmer who codes on a 24 inch monitor and uses my laptop screen as a secondary screen, can you elaborate on why an ultrawide is such a game changer? I mean its not like code is longer horizontally, more like vertically so I was just a little confused/curious about why you think that.

[@Crazy_CJ_]

@kots9718 most say you can see the ui elements better but I will disagree

[@Mayur7Garg]

@@kots9718 Code is longer vertically but most IDEs have more UI horizontally (sidebars, explorers, minimaps). Wide screen means you don't have to hide or disable them. Use of wide screen is mostly about having multiple things open at the same time that prevents a lot of switching and scrolling. For instance, you can open the blog you are referring alongside your code editor (or even split tabs on the same screen as in Edge) I use jupyter notebooks a lot and in Jupyter lab or VS Code, you can open multiple views of the same file side by side. No need to scroll too much to see what you wrote at the top. It is great for git diff for the same reason in any editor. Referring to any related file is easy for the same reason. I typically have one or two files and a terminal open side by side in VS Code at the same time. In most IDEs you won't have to close most things. You can just dock them to the very left or the right.

[@jeffrey5602]

DS here who managed to get an M2 Max with 96GB at work. Love the device. But don't kid yourself. You will not do any significant LLM training on this thing either. Its fine if you stay below that 1B weights range I would say. If you think about buying something for personal use just get a cheaper device + setup a local workstation and ssh into that. Will also help you build up your skills

[@kingamv107]

what do you mean? 1B weights would require 96GB? what about 8.72B params?

[@jeffrey5602]

@@kingamv107 No, it does not require that much, but compute is limited and for training on more samples you will probably want to change to the cloud anyways. Have not tried doing any finetuning of 1B+ models on the Mac

[@PiracyAgreement]

An underrated feature for Excel heavy users is the full-size arrow keys. For me, it's a non-negotiable.

[@Hundredthldiot]

I'm really not convinced about the case for LLMs on laptops, or even desktops. I don't work in AI, but conventional software engineering went through this over a decade ago with the transition from local and on-premise CI builds (e.g. Jenkins) to cloud CI (Travis, GHA and so on). I think it's beyond the scope of this channel, but if I was making this decision I would start with "what is it going to cost me in terms of cloud compute, and so what's the ROI period for a more expensive laptop capable of running the same work locally?". There are other benefits of running stuff on cloud, like greater flexibility in terms of instance sizing, and simply being able to close your laptop and let stuff run while you do other things.

[@themedleb]

For AI/ML, depending on how large the model you're working on, you can still use your laptop offline just for testing (if your computer can), once you're sure your model works, then you can offload it to the cloud and keep working on it there. This way the costs will stay low, because it doesn't make sense spending tons of money on something you're not sure when will it work or if it will work at all.

[@Hundredthldiot]

@@themedleb yes, but "it doesn't make sense spending tons of money on something you're not sure when will it work or if it will work at all" also applies to buying a super-expensive laptop. A year or two ago I bought a 32GB laptop with a 3060 GPU and quickly realised it was severely under-specced for the LLM experiments I wanted to do. Cloud is always going to be more flexible. That said, I defer to the experience of people that are doing this stuff professionally.

[@BenuTuber]

You are completely on point. While doing my PhD in ML I needed to change my machine far too often. With the recent LLM trend it doesn’t make sense to run models locally when there are so many cheap cloud solutions available. The price I’m saving by going basic MacBook can give me cloud compute for years. For some tasks running locally isn’t an option and for those that can be run locally, costs on cloud are almost free (because of it being a commodity and advantages of dedicated hardware)

[@themedleb]

@Hundredthldiot I agree that the cloud is much more flexible and isn't limiting, in my opinion, it is the best overall for these kind of things especially for a good price, but for your case, you should have analyzed your needs before assuming a certain GPU will fit your needs, that is not 100% your fault though, AI is progressing rapidly for the past few years, and with this progress the requirements are going high too.

[@Hundredthldiot]

@@themedleb Sure, but LLMs wasn't my primary use case, and it only cost 920 euros (Yoga Slim 7 Pro X, great recommendation by Josh!) so it was a cheap experiment. In the end the limitation of that laptop was battery life and general ergonomics so the Yoga is now desk-bound and I've gone back to my 2020 M1 MBA for mobile use. Honestly, I still think Macbooks are unbeatable unless there are application compatibility issues.

[@GunzyTech]

As a writer who trains our company's in-house AI models, I make it a point to watch each of these videos. Even when the content doesn't directly relate to my work, I find that there's always valuable insight for my laptop search.

[@JustJoshTech]

I really appreciate that

[@akin242002]

As a former Insurance underwriter turned IT analyst, most of the time you are in an office or working from home. 300 nits of brightness are perfectly fine. Lenovo ThinkPad T14 GX, Dell Latitude 7XXX, and HP EliteBook 840 GX will be the laptops assigned to most data analyst workers by their company.

[@CitAllHearItAll]

Giving anyone who consistently views tabular data a screen any smaller than 15” is a crime.

[@Mike-jb3xe]

Never think a laptop is good for AI / ML, you don't want a long running task living in your laptop, and make everything else awkward (annoyed by the heat but can't do a pause / resume, accidentally close the lid, etc). Would rather build and ITX pc and ssh into it.

[@cbernier3]

What job is going to pay to get you both an ITX and a laptop?

[@cbernier3]

Also the long running tasks don't live on the laptop. The laptop is for developing the models. You only need to know that your code is working on the laptop. After that, the work is done on the cloud. So your problem doesn't exist.

[@franciscowilhelm1083]

Using personas to calibrate the recommendations to prototypical use cases. Excellent quality video!

[@lhl]

Since I just spent quite a bit of time doing research and running tests, I just want to note that the M4 Max 40CU (top of the line), sadly only has 34.08 TFLOPS of FP16. This is roughly equal to a desktop RTX 4060 in terms of compute. A mobile RTX 4090 (which is pretty cut down from the desktop version) will still have twice the Peak FP16 Tensor TFLOPS w/ FP32 Accumulate (also, 264 INT8 TOPS for quantized inference). Based on 40 RDNA3.5 CUs, Strix Halo should have just shy of 60 FP16 TFLOPS (but only 256GB/s of MBW vs the 576 GB/s that both the Mobile 4090 and M4 Max have). For reference, a desktop 4090 will have 165.2 Tensor FP16 TFLOPS (FP32 Accumulate) and 1008 GB/s MBW. Bottom line: line, if you're doing local training, don't use a laptop unless you *really* have to.

[@akin242002]

I thought the M4 Max 40 core was getting 18 TFLOPs of GPU power. Still good enough to compete with the laptop version of Nvidia RTX 4070. 34 would be massively off from the sources I looked into. Disinformation to an extent on whichever side is off.

[@tsizzle]

But what if I don’t have to train the whole foundational model from the ground up? I just want to train the edge weight parameters using QLoRA and I’m willing to go down to INT4 or FP4 4-bit precision?

[@Ro1andDesign]

@@akin242002 18 TFLOPS of FP32 performance translates to about 36 TFLOPS of FP16 performance

[@akin242002]

@@Ro1andDesign Thanks!

[@lhl]

@@tsizzle QLoRA uses 4-bit storage data types (saving memory), but computation still happens in FP16/BF16.

[@jonathanofrivia4192]

today i was thinking that it would be good to see this subject on your channel and boom! here it is! thanks as always ❤

[@pewpewpower]

This is one of your best videos yet! Excellent content organization, pacing, charts📊, the detailed recs and scenario analyses. Just wow. Keep it up!👍

[@jonathantran7102]

7.5 minutes in.. just wow, amazing data, love how you present the different processors, and the tier chart for fan noise/heat is incredibly helpful

[@JustJoshTech]

Thanks for noticing :) I thought that fan noise chart would be a helpful graphic

[@copacialex]

Wait what?! I don't believe it is possible to use a laptop for something different than content creation. It should not be allowed! 'Sad' thing is that who needs a laptop for other stuff then 'EXPORTING MASSIVE 4K VIDEOS" they already know what they need or want. However, this is a great educational video and very professional. Thank you!

[@lilunchengsmiles]

As a machine learning engineer, the best laptop for my work right now is the Apple MacBook Pro. The primary reason is its unified memory, which allows me to load large language models directly into my 96GB M2 Max MacBook Pro and get used by GPU-something no non-Mac laptop currently offers. This feature is invaluable for prototyping and testing models efficiently. Secondly, for machine learning work, we often spend significant time manipulating data in Pandas data frames, and for reasons beyond just clock speed, the Apple M-series chips consistently outperform x86 chips in these tasks. With the release of the M4 chip, this performance gap has only widened. Lastly, Windows simply isn’t ideal for data science. Many libraries don’t install smoothly on Windows due to various compatibility issues. While the Windows Subsystem for Linux (WSL) is helpful, achieving the full benefits often requires maintaining a separate Linux OS, which adds complexity.

[@higochumbo8932]

So, as someone who is trying his best to avoid Macs, I guess you'd probably say it's better to wait for Strix Halo laptops with unified memory rathern than buying a Windows laptop now.

[@lilunchengsmiles]

@@higochumbo8932 Generally, aside from a MacBook Pro, I prefer either a cloud setup or a desktop workstation, at least 96 G mem with an a NVIDIA GPU such as RTX 4090. However, if you really want to use a laptop, and your projects don’t involve large language models, I’d still recommend a laptop with an NVIDIA GPU and it is running Linux. Window is extremely bad for machine learning. If you really want to use window, I strongly recommend Window Subsystem for Linux - 2 (Window 11 has it by default)

[@bobhob35]

@@higochumbo8932honestly as someone with a windows laptop with 64gb of ddr5 ram and an intel 13th gen cpu who’s finding it very frustrating working with windows and a cpu that gets very hot I my move over to a MacBook now I can buy a mid tier pro model with 48gb of ram.

[@bobhob35]

However I’m waiting for the new AMD processors to come out so I can make an impartial comparison and educated purchase. Although I’m done with intel 😮

[@higochumbo8932]

@@bobhob35 What are you finding frustrating with Windows? Or is Intel your only issue?

[@Ty.mauricee]

I just started studying data science so this is the perfect video for me, thanks josh👍🏾

[@PKperformanceEU]

Josh, just because most 13gen intel laptops are loud and hot doesn’t mean all are like that. I have the asus zenbook pro 14 ux6404vv with 4060m And yes i had to replace the thermal paste and i use a self made fan control-curve software i wrote in C# Now i can exactly control the fans the way i want. Its a 13700H laptop, gets 108/1112 on CB2024 and GPU can sustain 125w. Its very quiet unless pushed and now with my controls always cool to the touch! By the way its for sale since i move to macOS. Region Europe

[@nilaypatel918]

Kudos Josh for bringing up this content!

[@JustJoshTech]

Thanks!

[@edreilima230]

Tysm Josh! I waiting soooo long for this video!

[@YuraL88]

Numpad is the most important requirement whenever I buy a laptop, a 16'' display is rather a nice bonus. I do some basic ML/scientific calculations using Python libraries, so for me CPU, especially single-core productivity is super important. I bought a Lenovo ThinkBook 16 G6 IRL, it's perfect for my needs. - i7-13700H up to 5 GHz - 32 GB DDR5 5200 - 1 TB NVME SSD I don't know why you pay so much attention to screen resolution, it's important for large displays >21'', but for laptops, it seems overvalued.

[@Sci-techsaga]

If I use the cloud for training do i need nvidia minimum 4050 graphics in my laptop

[@CPA003]

Just wacthed this 4 minutes after it was posted!

[@nayyarrazakazmi8386]

Excellent as always Josh. Please also review laptops for heavy AI based video editing workflows like using AI based functions in Davinci Resolve Studio

[@Ccb559]

Are you a mind reader? So glad I found your channel recently and this is exactly a topic I wanted to know more about. Awesome but trippy. Great channel by the way. Keep it up. You got my subscription.

[@wlcrutch]

Wow, super helpful examining real use cases in such detail!

[@malamute4793]

I just bought a 10-year old laptop with I7 for just 50 bucks. I will use it for AI

[@novam2580]

Incredible video man. Subscribed!

[@manishmalhotra3]

Loved the Analysis ❤ Great Work Just Josh 👌👌

[@maruthi_singh]

Thanks for making a video on laptops for programmers, really appreciate your effort 👏👏. I have commented on a previous video asking for laptops for people like me and here it is .

[@TheKdcool]

This type of review would be awesome if it included benchmark from LM Studio running local llm and Automatic 1111 running stable diffusion :)

[@LiaVeja]

Thank you for this video! I was looking forward to it!

[@tsizzle]

This was the video I’ve been waiting a long time for!!! Thank you!

[@notaras1985]

Are you excited for the Blackwell equivalent of the Ada5000 GPU? When is it coming out

[@JT11111]

Josh 2:20 the laptop is a Zephyrus g14 not a Yoga pro 9i

[@SergiiTorchukUA]

It seems 16" macbook has no real competition.

[@johnconstantine5228]

Lenovo legion 9i

[@SergiiTorchukUA]

@johnconstantine5228 Legion is not an option: unprofessional look, bulk, huge powerbrick, keyboard with numpad offset to the left, bad touchpad, screen is subpar. I'm considering Thinkpad P1 gen 7, but it has its own flaws (powerbrick, screen). And I'm not even touching the subject of working on battery. Unfortunately, hardwarewise, I don't see any real competition to Mac.

[@mawial2430]

When it comes to handling large datasets, the Mac is game changing. I would regularly analyze large datasets over 10K in size and on my 9th gen I9 windows machine, the fans would spin up like crazy and and would take forever just to even manipulate the files. Then my company got me an M2 Max, and the Mac was able to run the same operations without breaking a sweat.

[@KB-wb9ym]

Excellent video Josh, you nailed it mate! Thanks, Chandu

[@oremad7055]

I have to say this method of describing different individuals and their needs regarding their tech is a pretty straightforward and efficient method of helping put perspective in new buyers. You and your team nailed it.

[@maruthi_singh]

Any ThinkPad that can be added to this video??

[@haorantao612]

Can't wait for more options in the 2-in-1 laptop category...

[@atom608]

Unless my job was in finance I would never get a numbpad laptop as the shift to the left really screws up your typing and even after a while its hard to orientate your self without looking at the keyboard sometimes imo

[@sethdisner3335]

This video was PROFOUNDLY useful. I did wonder why the Samsung Galaxybook4 Ultra wasn't included since it's light, has a long battery, and also a RTX 4070. I had been leaning towards one but since i'm in more of an "Agnes" role. Was that just an oversight? Or is it not recommended for a reason?

[@sofimohd3867]

I came across this channel just when I was looking into potential laptops for my university work and this is a gem! Wondering if you could also cover laptops catered to the drug discovery and bioinformatics sector, as they require mid to high laptop specs. I'm a student that does casual tasks but also working on some 3D visualization softwares, programming and may do slight gaming, but require a lightweight chassis. Are there any suggestions? I was looking into G14 2024 but don't know if it still has warm issues after the updates now. Thanks for the informative videos, love it and subscribed!

[@samuelmelo483]

This video is a gold mine. Thank you Josh!! Will subscribe immediately.

[@freedom4341]

Nice video as always. Maybe you can do a video next time about best 2in1 laptops.

[@_wallykhalid]

Finally! Love this niche content!

[@NoobitoRUS]

On 12:29 the wrong laptop is highlighted in the graph

[@ashs247]

Really good look at these devices for these workloads, but would’ve loved to have seen some commercial Windows PCs compared rather than their consumer counterparts.

[@KellyWu04]

Nice video. I agree with most of the points.

[@ericneo2]

Thanks for the Aftershock shout out. I remember when Metabox was the leading Clevo seller. Now days they are incredibly expensive.

[@corey7219]

3:06 having a high refresh rate display lessens the strain on your eyes when you see moving content on screen. Which makes a big difference while I'm at work

[@ugchris]

Please make a video on the best laptops for UI/UX DESIGNING and CYBER SECURITY. Thank you. I love your works. You narrow all explanations to the best way.

[@WHMan02]

Hey Josh, i am a AI developer student from Europe. Have you any recommendation for a 14 inch system? If so, i myself like to work with mlflow operation in docker images. Powerful enough for PyTorch and Tensforflow and Keras DeepL operations.

[@guizarbayardoemmanuelisaia1718]

What about the NVIDIA RTX 6000 Ada Generation?

[@indylawi5021]

Thx for the great coverage. My workflow involves running AI LLM Model locally as well as Data Analytics/Science. I am considering either the latest Intel/AMD CPU + nVidia GPU or the MacBook Pro with M4 Pro to upgrade my current setup.

[@tsizzle]

I think my use case will be more like Agnes. I’d like to run LLMs and perhaps other multimodal models (and perhaps Stable Diffusion for imaging) locally. For the purpose of not having to pay per token and for privacy concerns with Cloud services, I really like to run both the model (training/inference) locally. I heard that while inferencing is not that memory intensive, it needs high memory bandwidth (256bit memory bus, 576GB/s bandwidth on the 4090) I need something powerful enough to fit the models like a Llama3.1-70B on to the GPU. I’m looking to fine tune with QLoRA and potentially incorporate a RAG with a vector database. Would a mobile 4090 with 16GB VRAM be enough, or will I need to only look at GGUF quantized models and reduced FP4/INT4 4-bit precision, and paging optimization to fit everything onto the GPU. If that’s the case, could I go to a 4080 with 12GB VRAM or 4070 with 8GB? I also need to be able to run multiple VMs the laptop and maybe setup a kubenetes cluster for testing purposes… maybe 2-3 nodes. I need to do it all locally on the laptop and bring it to meetings where the company is air-gapped where I can connect to projector (via display port of HDMI) to demo everything and show my Jupyter notebooks.

[@jeffersonmp4]

The question is which OS is better for Data Science Mac OS, Windows or Linux? This could affect the purchasing decision

[@drdudewin]

If you are connecting to a super computer server like in the 2nd example from Josh, then it doesn’t really matter which OS you use to initiate the connection. In my experience using both a windows laptop and a macbook, I can tell you that the macbook experience has been smoother and connectivity seems to be more stable overall. A simple example for why a macbook might be more preferable is to imagine never having to restart your laptop and as soon as you open the lid continuing your analysis work on a completely silent environment…

[@CIL-mm6su]

Would the acer predator helios neo 16 and comparable laptops like legion, rog, etc do well with AI/ML/DS/DA tasks? The specs seem on par with the 7i and 9i mentioned. I'm wondering if its moreso the portability of the yoga laptops that onto this list or what the biggest defining feature that made them a favorite.

[@hongtruong5516]

I noticed you didn't mention the Legion 7i here, is there a reason it wouldn't fit this category?

[@rob_101]

Is acer swift go ultra 5 good for beginner in data science?

[@nasatech6272]

What you think M4 pro macbook pro is good for machine learning ??

[@ayushthapamangar801]

Is mac mini m4 good choice?

[@NoTyranny1501]

Thanks so much for this video. One question: is there any reason you like the Lenovo Legions instead of the Lenovo Thinkpad p16? Thanks!

[@AdnanAhmed-nh4dm]

Should i go for the zenbook 14 amd (2024) or the zenbook 14x if i study software engineering and i want a bit of light gaming or is the display too big of a trade of compared to the 14x

[@mikayel18ify]

i've tested the surface laptop 7 witgh snapdragon on power bi and the performance surprised me, even while being emulated from x64 to arm64. basically can sustain intels last gen H series processor's speed ...

[@theswansons8527]

Question. I’m a Mac user who would like to run AI programs like Alphafold locally. (I also use a super computer.) Will max configuration likely allow me to do this?

[@mwd6478]

DS reporting in. Yes to Python locally, no to LLM locally. Love the channel!

[@charlsmv]

Awesome as usual

[@PerpetualPreponderer]

Another great one, nicely done Josh! Can i pick your brain for just a bit...I'm looking to switch to an M4 Pro once your reviews are out & provided everything is fine. I currently virtualise windows on ARM on my mac & run Power BI Desktop therein, whenever I need to work with it...16GB is jusssst about right but teetering on the edge for sure....what is the best value model of the M4 Pro you'd recommend? I'm fine spending a little more, but wanna get the best therefrom...

[@noname5905]

What does it mean to say programs like Tableau (for example) do not “run natively on the hardware” but they do “run under emulation” ?

[@TEDMinis]

I am going to college next year and I plan on getting my masters. I want to learn cyber security for my undergrad, and then machine learning for my masters. When choosing a laptop, there are lot like you have mentioned, but I was mainly looking at the 16 inch macbook pro m4 pro chip, vs the Lenovo Yoga pro 9i. I would get the macbook, but the problem is the operating system. I know for buisnesses they mainly use windows and not that much on Apple. I heard that they are moving a little bit with Apple. Do I get the Lenovo Pro 9i, or the 16 inch macbook pro m4 pro chip?

[@pHZerœ]

Finally someone made it 🙌 ❤

[@AbdullaAbdulla-hv6oj]

Pls low budget data scientist for laptop recommend?..😢

[@emsees32]

Genuine question: Is there even a point in trying to find a job as a junior software developer? I graduate soon and considering the speed in which AI is skyrocketing, I don’t think I’ll be able to compete. I’ve already been rejected multiple times.

[@JustJoshTech]

Yes it definitely is. I started as one. Have you watched my business channel? There is alot there about resumes and cover letters?

[@emsees32]

@@JustJoshTech thank you so much for replying. I have not, will do! If it’s not too much to ask, is ~1300USD reasonable for an Ideapad 5: 8845HS, 16GB, 1TB, RTX 3050? Alternatively ~1100USD with 32GB, Radeon 780M. Been checking your videos and website but most of your recommendations are unfortunately twice as expensive outside of the US.

[@JustJoshTech]

@emsees32 it is too expensive imo

[@JustJoshTech]

@emsees32 I would wait for Black Friday sales if you can

[@emsees32]

@@JustJoshTech super grateful for the replies! Yes, that’s what I had in mind! Subscribed to your second channel as well. Cheers👍🏻

[@Alireza1Shayan]

do you recommend hp dragonfly g4? just for data scientist and data Analysts؟

[@AbdullaAbdulla-hv6oj]

Ai and ml and data science for buget one Mac book and one windows laptop pls recommend?

[@navinr8160]

Great content!

[@cleandata_sk]

awesome video. I am data analyst doing some data science tasks as a side job and I decided to go with pc instead of laptop. the computational power you get for the same budget is just worth it if you are working from home. nevertheless lenovo models were in my list when i was doing research + ASUS ROG Zephyrus

[@azhyhama9649]

Josh, you can get 96+ GB of RAM in P series, some have 3k display 14.5-16 inches. But their Dgpu choices are limited. What's your opinion?

[@NetvoTV]

Matte screen allow you to see your content without cranking your brightness too high which might hurt your eyes, right?

[@cbernier3]

It's also gonna depend on if it's your home laptop or work laptop. Your full time job work might be all cloud sure. But what about your own projects? Part time freelancing projects. Passion projects. Start up projects, etc. You can't use the same laptop as your day job for those, so in that case you want your home laptop to be able to double as second job work laptop.

[@rohitquat]

Exactly what I needed

[@JackHarley-f8i]

Thanks for coming to our school today!

[@JustJoshTech]

You got it Jack!

[@enricocompagno3513]

What about the hp fury g11? I didn’t see it in any of your reviews

[@Technerd207]

As a student in the industry I chose the value options (saving about 2K compared to what is shown here while still getting a fast 16GB gpu ;D) and got a Thinkpad yoga L13 g2 for 150$ and a PC with a 14 core xeon, 128GB ddr4 and an rx 6800xt for 600$ (both second hand). I also use Arch btw.

[@Camexplode]

What aboit the P16 Gen2 maxed out?

[@SamuelKiriama]

Good job Josh. You've literally saved my career 💯

[@MaoHUN]

Hi Josh, at 2:22 it's an asus laptop isn't it?

[@Namu4Life]

How about the legion 7i gen 9 with the 3.2K display, i9-14900HX, RTX 4070, 32GB, and 1TB? Would that laptop be good for this kind of video?

[@Sleight-l4y]

IDK feels pretty stupid buying a laptop with a 40 series card when we know that the 50 series is coming out within the next few months

[@MnemonicCarrier]

Lenovo Legion 5 laptops are pretty good for data science 😉 (especially if you're training/running LLMs locally).

[@leonardomurakami5125]

What do you think about the Legion Slim 5 with Processor Ryzen 7 8845HS, 32GB RAM 5,600Mhz Ddr5 and Nvidia RTX 4060?

[@MnemonicCarrier]

@@leonardomurakami5125 I'm very close to pulling the trigger on that exact model. At the moment I have a Legion 5 gen 5 (Ryzen 7 4800H), and a Legion 7 gen 7 (Ryzen 7 6800H). The Legion Slim 5 will most likely be my next purchase (without a month or so). I just really hope it plays nice with Linux.

[@vahidehsanzadeh9424]

Great video!! Can you make one for engineers?

[@Harveyway]

I bought a laptop with integrated graphics, 32gb of ram. I plan to get a external gpu when i start learning morr graphics intensive tasks. How viable is this plan? Or im i really better of getting another machine?

[@prnva_]

Why run natively?

[@williamyoutube368]

Hi Josh, thanks for your great insights. What’s your opinion on the Lenovo X1 Carbon Gen 12 and Dynabook Portege X40L-M?

[@aras_aras_aras_aras]

Great video! I would include ASUS ProArt PX13, which is a unique 13" convertible laptop with RTX 4060 GPU, and Zephyrus G14, which is a good all-round 14" laptop with RTX 4070 GPU.

[@kennethprime90]

Curious what about Thinkpad p/t series? I just picked up a thinkpad p16s gen 3 4k OLED for $1150 just waiting for it to ship out.

[@prison9865]

Hi, can you clarify with your team about the gpu. As far as I know, gpu helps to train models quicker using such libraries as tensor flow or py torch. Those were optimised to use a gpu. Other classical algorithms such as random forest, xgboost, glm, gam is not optimised for a gpu. For most of us, we probably won’t be using tensor flow, so predators spending lots of money on gpu would be wasted money

[@tsizzle]

Actually, Xgboost can run on CUDA. I’ve done comparison training runs between xgboost running on GPU vs. cpu and GPU runs much faster.

[@Oldbettie]

Unfortunately nothing comes close to the battery on a Mac. I just made the expensive mistake of testing a new Intel lunar lake machine with Linux I had to charge it twice a day for 2 days before I decided to return it. 😅 $1000 mistake losing 20% on the restocking fee 😅

[@Oldbettie]

Fyi, I tested the yoga pro 9i

[@Dj_Ropes]

Look into thinkpad x1 carbon. Getting them used is cheap Nd battery life is great.

[@andyH_England]

I am currently seeing another round of creators-sponsored "reviews" for X-Elite laptops and they are still quoting 24-28 hours of battery life, trying to fool people. I have tested them all and none come close. I would say 8 hours of SOT doing multitasking work, not watching movies. I get 50-100% more SOT on my Macbook. I suspect your example with the new Intel is more informative than the recent wave of sponsored ads by YT creators quoting ridiculous unreal numbers. As you say, Macs are the best for efficiency especially when multitasking.

[@spiderman1537]

Thank you sir.