3 ms·

> I suspect your main motivator is to want to build a PC. Chances are that the extra computational power or cores won't actually make your work faster for day t

> I suspect your main motivator is to want to build a PC. Chances are that the extra computational power or cores won't actually make your work faster for day to day use. Another potential factor - If you play games or develop 3d applications, high end GPUs are much more affordable in desktop form factors than in laptop ones and generally have more horsepower than their laptop equivalents. If you do a lot of video encoding for live streaming or media creation you may also benefit from this (seriously a GTX 1080 can encode 1080p HEVC at 300 FPS). The cost is also generally lower for comparable hardware if you do a DIY desktop than a laptop. While this might not be relevant to OP, it may be relevant to other people in a similar situation.

inetsee 10y ago
This may not be relevant to the OP, but I'm interested in machine learning, and having a graphics card with a big GPU is important. Serious machine learning computers have sky high prices, but for learning, you can get a graphics card with 640 or more gpu cores for $250 or less. I don't know of any laptops in the requested price range that have that kind of gpu power. Of course, a big graphics card can also be used for graphics work, including video encoding, not just video games.
- problems 10y ago
  Yeah, there's a quite broad spectrum of GPGPU applications these days, Wikipedia has a fairly comprehensive list to consider: https://en.wikipedia.org/wiki/General-purpose_computing_on_graphics_processing_units#Applications https://en.wikipedia.org/wiki/General-purpose_computing_on_g... If you're interested in any of these and don't need the mobility for another reason, a desktop may be a very good consideration indeed.
- cr0sh 10y ago
  For my current involvement of taking the Udacity Self-Driving Car Engineer Nanodegree, I'm currently using a 750 Ti SC card - which was what I had in my current workstation at home. I've found that it performs adequately (so far) for what I am doing. I'm running Ubuntu 14.04 LTS, and I installed CUDA and CUDANN (and other junk) to run Python/TensorFlow with GPU support. That said, I'm currently working on the behavioral cloning project (teach a virtual car to drive using what we've learned so far), and given the amount of data we'll gather (10s of thousands of frames we have to process) to train our models - I tend to wonder if I will need to upgrade. Fortunately, that won't be too difficult for me - I have both a 960 and 970 waiting in the wings; they were cards originally destined for a mini-itx gaming machine build that I've never gotten around to (hmm - maybe I should just build it for my next workstation instead). Also - 960s and 970s are dropping in price like a rock, since the hardcore gamers are moving to the 10xx line; while they may not have as many cores or as much speed, they are still very capable cards for ML purposes. I've also been considering getting a multi-pcie slot server motherboard (maybe with dual xeons or such - these mobo combos are actually pretty cheap on ebay), and dropping in one of those cards, then later adding on more GPU cards - strictly for an ML box...

inetsee 10y ago

This may not be relevant to the OP, but I'm interested in machine learning, and having a graphics card with a big GPU is important. Serious machine learning computers have sky high prices, but for learning, you can get a graphics card with 640 or more gpu cores for $250 or less. I don't know of any laptops in the requested price range that have that kind of gpu power. Of course, a big graphics card can also be used for graphics work, including video encoding, not just video games.

problems 10y ago
Yeah, there's a quite broad spectrum of GPGPU applications these days, Wikipedia has a fairly comprehensive list to consider: https://en.wikipedia.org/wiki/General-purpose_computing_on_graphics_processing_units#Applications https://en.wikipedia.org/wiki/General-purpose_computing_on_g... If you're interested in any of these and don't need the mobility for another reason, a desktop may be a very good consideration indeed.
cr0sh 10y ago
For my current involvement of taking the Udacity Self-Driving Car Engineer Nanodegree, I'm currently using a 750 Ti SC card - which was what I had in my current workstation at home. I've found that it performs adequately (so far) for what I am doing. I'm running Ubuntu 14.04 LTS, and I installed CUDA and CUDANN (and other junk) to run Python/TensorFlow with GPU support. That said, I'm currently working on the behavioral cloning project (teach a virtual car to drive using what we've learned so far), and given the amount of data we'll gather (10s of thousands of frames we have to process) to train our models - I tend to wonder if I will need to upgrade. Fortunately, that won't be too difficult for me - I have both a 960 and 970 waiting in the wings; they were cards originally destined for a mini-itx gaming machine build that I've never gotten around to (hmm - maybe I should just build it for my next workstation instead). Also - 960s and 970s are dropping in price like a rock, since the hardcore gamers are moving to the 10xx line; while they may not have as many cores or as much speed, they are still very capable cards for ML purposes. I've also been considering getting a multi-pcie slot server motherboard (maybe with dual xeons or such - these mobo combos are actually pretty cheap on ebay), and dropping in one of those cards, then later adding on more GPU cards - strictly for an ML box...