What is the best budget GPU for training deep learning models?

just adding my experience here... i spent weeks trying to optimize a semantic segmentation model on an 8gb card last year and it was basically constant frustration. i finally swapped to a 12gb card and the difference in peace of mind was huge. even if the 4060 has better architecture and is faster on paper, that wall you hit with vram is hard and unforgiving. i usually check the lambda labs blog or tim dettmers' guide for gpu picks before buying anything, they are the gold standard for this stuff. basically, more vram lets you breathe and avoids those random crashes. here is why i think you should play it safe:

• batch sizes. running batch size 1 is slow and doesn't converge well. 12gb lets you use actual batch sizes like 8 or 16.
• image resolution. 8gb fills up fast when you go from small 224x224 to 512x512 images.
• reliability. nothing is worse than a crash 10 minutes into an overnight run because of a memory spike. i'd look for the ASUS Dual GeForce RTX 3060 V2 OC Edition 12GB GDDR6 or even the ZOTAC Gaming GeForce RTX 3060 Twin Edge 12GB GDDR6. they usually go for around $285 now which stays well under your $350 budget. honestly, saving that extra money lets you grab more system ram or a faster nvme for your datasets. since you need it by next weekend, shipping to seattle is usually super fast from most stores anyway.

Building on the earlier suggestion, im satisfied with these:

• ASUS GeForce RTX 3060 12GB: 192-bit bus, way better for datasets.
• Cloud renting: Pro service, better TFLOPS/dollar than local DIY.

> Is the extra 4GB really that big of a deal In my experience, VRAM is king for training. Ive hit OOM errors way too often with 8GB cards, so grab the MSI GeForce RTX 3060 Ventus 2X 12G OC.

Honestly, I think the higher VRAM is probably your safest bet for computer vision. Someone told me that larger datasets can really screw you over if you only have 8GB. Not 100% sure if the speed boost on newer tech actually makes up for it, but Id personally prioritize memory to avoid those annoying OOM errors. Its a tough call tho... maybe consider cloud rentals if hardware prices are too high?

Re: "Like someone mentioned, everyone is leaning toward the..." - honestly, the market is a total mess right now! I remember training vision transformers on my old rig. I was so torn between the newer architecture and older cards with high memory. I went with the older gen because that extra memory meant I could double my batch sizes! It was absolutely amazing to see my throughput jump. I love seeing how older Nvidia silicon still holds up... definitely go for more memory.

Like someone mentioned, everyone is leaning toward the older card for the vram but i disagree that its the only thing to look at. tbh, as a beginner, i worry way more about things like heat and power spikes causing crashes during a long training run. the 40 series is much more efficient and i'd be concerned about the longevity of an older card, especially if you're on a tight deadline for your project. just to clarify a few things tho... what kind of power supply are you working with in your setup? also, are you planning on building your own custom models from scratch or just fine-tuning stuff like yolo? if you're just doing small-scale stuff, the 8gb might not actually be the bottleneck you think it is.