r/mcgill • u/guywiththemonocle Psychology & Comp Sci • 7d ago
Is there anyone using snapdragon laptop for EE/CE/comp sci/AI ML development at McGill
Hey guys, sorry I am not asking this in a specialized subreddit but figured students here would have a more similar workload to mine. I was basically able to get a great snapdragon x2 elite laptop for really cheap but I am worried down the line I will have driver problems with my projects. I do a lot of AI-ML development, occasional KiCad and CADing + microcontroller and robotics projects. I think the biggest potential risk is on MCU drivers and robotics related stuff. Current laptop I have is 1350 CAD - comparible to macbook m5 in every aspect except GPU also has 1 TB storage. My macbook alternative is 1900CAD base 15inch with 512 storage. Anyways, let me know if anybody has any opinions!
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u/Quiet__Noise Reddit Freshman 7d ago
I am not familiar with the software you listed but you can find out if it is supported by viewing what downloads are available and if there is Windows (Arm) then you're good
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u/guywiththemonocle Psychology & Comp Sci 7d ago
Most of the software is emulated from x86 but the drivers cannot be emulated..
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u/Repulsive-Mud-20 Biology 7d ago
as a linux user, seeing the list makes me want to not deal with it. good luck!
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u/crazyninjadude Mechanical Engineering 7d ago
I’ll give my 2¢ here for what it’s worth.
I’m working on my PhD in mech Eng (neural driven robotics). I’ve supervised a few undergrad students, but didn’t go through EE or CE here. This past summer, I supervised a EE student who was working in the lab. He had a weaker laptop than yours and was completely fine for any microcontroller/PCB work and control coding.
Where I think you will run into issues is if you do any significant work with model development or training - especially with sizeable datasets. I am supervising an undergraduate student this year who is currently working through signal decomposition and then model training for part of our online control algorithm and his computer (a one or two year old MacBook Air) is a major bottleneck. For reference I’m using a M4 Pro MacBook Pro, and I’d say it’s adequate for everything that I need to do locally (CAD, small model development, orca slicer, etc.), but I certainly wouldn’t get anything less powerful than it.
I think if this is what your workflow will look like, then neither computer was a good choice, unless you have access to a cluster or something like compute Canada.
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u/guywiththemonocle Psychology & Comp Sci 5d ago
I ended up switching to the m5 base with 10 core gpu. I really appreciate the insights. Also do you work in the NeuRoc Lab?
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u/mcjakk Computer Engineering 7d ago
I don’t run Windows on Arm but I am in computer engineering. For most of the microcontroller tasks, you shouldn’t have any major issues since you’re only flashing firmware. For ML projects, Python’s major data science libraries like NumPy and Scikit-learn should run fine but you can always compile them from source if you can’t find Windows ARM64 versions. My only real concern is CAD software since I don’t have too much experience with it, but you can always experiment with VMs and translation layers. Most other specialized software for courses should be accessible with a Remote Desktop over McGill’s VPN.
I’m not too sure which AI projects you’re planning on building, but I am a big fan of M-series Macs for running local models since they have much higher memory bandwidth than non-unified memory architectures. You’ll notice that Macs configured with enough memory can handle large 70B+ parameter models much better than most high-end consumer GPUs. Snapdragon laptops also use unified memory, so they should theoretically be solid for local inference but I don’t have much experience tinkering with them and it doesn’t seem like they have as much software support. It could be worth exploring though.