Row 8596
Content Data
This page contains data entry 8596 from the Axioma AXP content repository. The structured data below represents the complete record for this entry.
Basically, if you're resourceful you don't NEED to upgrade. If you're learning, most models can be run just fine on 4GB of VRAM. If you want to use larger batch sizes during minibatch gradient descent, you can further subdivide minibatches into subbatches and accumulate gradients as you perform several smaller forward passes (just to address the possibility of anyone complaining about being limited to small batch sizes). If this still isn't enough you can pay for cloud GPU which is very cheap compared to a new PC.
If you'd prefer to own your own hardware, imo the minimum hardware that would warrant buying would probably be a 3090 for that sweet 24GB VRAM, a decent CPU with 32GB+ RAM. Anything less and you really might as well just pay for cloud compute, because the current setup you have will perform fine for most simple deep learning tasks.
| Field | Value |
|---|---|
| text | Basically, if you're resourceful you don't NEED to upgrade. If you're learning, most models can be run just fine on 4GB of VRAM. If you want to use larger batch sizes during minibatch gradient descent, you can further subdivide minibatches into subbatches and accumulate gradients as you perform several smaller forward passes (just to address the possibility of anyone complaining about being limited to small batch sizes). If this still isn't enough you can pay for cloud GPU which is very cheap co… |
| label | r/deeplearning |
| dataType | comment |
| communityName | r/deeplearning |
| datetime | 2024-05-19 |
| username_encoded | Z0FBQUFBQm5Lakw0M3FfUWdxUDNyZE1Wa0tZTHpsT0l6Qks0YTVWZGwycmJNNXhZYWJHZGxYN0dael9MeDRIRC1ENjJIYUFfNkx5d0hha3p3UlYtVUl2LWxJQ2UtREdFZ3hmWTd5Si1PMTZxSGVvSzhjT2UwczA9 |
| url_encoded | Z0FBQUFBQm5Lak9IcmlwQzNSd0tlODRUOVB5M3hLVnpGLVltWEFrcmI3M0h5Y1d5VmJFNzU4bTZYVDRXcVVuTXFmTW53S2xvTnhOYnM5LUZiTjJFdC1oUmFOX1dYT2M4c3h6SERmUkFZY25HSFowSTRBMjlwY2FLOUtmQ0ZqdjVfN0hHNVNmdWNzZ296RkJZTVBrcGFobWZScE1rSU1sUWF4SUxyWWgycG1XQU5kcGQ5VGJZdFpUcHhjbVAycHpDbXBJelNwSlhtcG1rR0k1Y21YdW82eVR2QngwUzFBNmo3UT09 |
Raw Record
{
"text": "Basically, if you're resourceful you don't NEED to upgrade. If you're learning, most models can be run just fine on 4GB of VRAM. If you want to use larger batch sizes during minibatch gradient descent, you can further subdivide minibatches into subbatches and accumulate gradients as you perform several smaller forward passes (just to address the possibility of anyone complaining about being limited to small batch sizes). If this still isn't enough you can pay for cloud GPU which is very cheap compared to a new PC.\n\nIf you'd prefer to own your own hardware, imo the minimum hardware that would warrant buying would probably be a 3090 for that sweet 24GB VRAM, a decent CPU with 32GB+ RAM. Anything less and you really might as well just pay for cloud compute, because the current setup you have will perform fine for most simple deep learning tasks.",
"label": "r/deeplearning",
"dataType": "comment",
"communityName": "r/deeplearning",
"datetime": "2024-05-19",
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}
Entry Information
- Entry ID: 8596
- Repository: Axioma AXP
- Dataset: arrmlet/reddit_dataset_36
- Total Entries: 100,000