![]() Unless you want to pay $2500 or more, the RTX 2080 Ti is the obvious choice. If you want to do your own research and choose a good bang-for-your-buck GPU, then checkout and select the highest performing GPU in your price-range. There are some great blog posts about choosing the right GPU for your needs.Īssuming you want a high-performance GPU, I recommend avoiding the marketing-noise and simply purchase the RTX 2080 Ti. You should always decide which GPU(s) you want first: everything else in your rig will depend on this decision. The GPU is the most important component of any deep learning machine. My build does not use blower-fan GPUs (cheaper), but blower-style may yield better performance. ![]() With the RTX 2080 Ti, watch out for overheating issues.EVGA or MSI) GPU, not the Nvidia Founders Edition The RTX 2080 Ti is benchmarked as the best GPU under $2500.Components are listed in order of their performance impact on training deep learning models. When choosing components like GPU, RAM, CPU and motherboard, there are three goals to keep in mind:īelow I list each component in our build and considerations for each. Cooler (Keeps the CPU from over-heating)Ĭorsair Hydro Series H100i PRO Low Noise, $110Īfter a $280 discount via Newegg’s business account and omitting tax, the total cost for all components was $6200 (+$107 for PSU upgrade). I increased to 1600W and all issues resolved. Warning: The 1300W PSU caused rebooting for distributed ImageNet/ResNet50 benchmarking. Update: Now I use (cheaper) Rosewill HERCULES 1600W Gold PSU, $209 (03/21/19) Seasonic PRIME 1300W 80+ Gold Power, $240 Seagate BarraCuda ST3000DM0 RPM, $85 128GB RAM (Random-access Memory)Ĩ sticks of CORSAIR Vengeance LPX 16GB, $740 ($185 each) 1300W 1600W PSU (power supply) Samsung 970 EVO 1TB - NVMe PCIe M.2 SSD, $250 () 3TB Hard-drive (Spinning disk internal hard drive for slow-access persistent memory) Update: In recent builds I use these (2x faster, half storage, similar price) options:ġ. Other option: CORSAIR CARBIDE AIR 740 ATX Case, $149 () 2TB m.2 SSD (solid-state drive for fast-access persistent memory) Intel Core i9-9820X Skylake X 10-Core 3.3Ghz, $850 X299 Motherboard (all other components attach to this)ĪSUS WS X299 SAGE LGA 2066 Intel X299, $492.26 Case (the box everything goes inside)Ĭorsair Carbide Series Air 540 ATX Case, $130 ZOTAC Gaming GeForce RTX 2080 Ti Blower 11GB, $1299 () 20-thread CPU (processor) ASUS GeForce RTX 2080 Ti 11G Turbo Edition GD, $1209 ()Ģ. Update: To avoid overheating, I now use these blower-style GPUs:ġ. Every little bit helps keep L7 going: thank you! 3 EVGA RTX 2080 Ti GPUsĮVGA GeForce RTX 2080 Ti, $3570 ($1190 each) Here is each component and the price I paid ( Jan 31, 2019): Support L7 by purchasing parts via the Amazon links below (zero-added cost to you). All the components of the deep learning workstation. If you have a local MicroCenter store nearby, they often have cheap CPU prices if you purchase in a physical store. I ordered everything online via Newegg, but any vendor (e.g. This post provides a good build and if while reading this you see an improvement, please comment below! Every Component in the Deep Learning Rig Even if there was, the perfect build is constantly changing as new hardware is released. There is no perfect build because everyone’s needs are different. I used PyTorch’s ResNet50 training on ImageNet to benchmark. Throughout, I include examples where you can reduce component costs further.Īt the end, I provide time and cost benchmarks of this machine versus a Google Compute Engine (GCE) Deep Learning VM. To help other researchers, I’m sharing the details for every component so you can build one as well.Įven if you are building a smaller deep learning machine or your first computer, you may find the component explanations in this post useful. That’s why I built my own version with similar or better components for $6200. This is a great machine for top-tier state-of-the-art deep learning research, but not so great if you’re on a budget. Instead, I found websites to purchase pre-built rigs like the Lambda GPU Workstation. While hunting online for how to build it, I couldn’t find a blog that was detailed enough to buy every component. ![]() I built a multi-GPU deep learning workstation for researchers in MIT’s Quantum Computation lab and Digital Learning Lab. ![]() Update: A better build is available in this post. Added Blower-style GPU, faster/cheaper M.2 SSD, and other options. Updates: Amazon links added for all parts. This post is in the all-time highest ranked posts on Reddit in the r/MachineLearning forum.
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