contrastive-learning-2023 is an open-weight model from Patelnikita, released under Creative Commons Attribution 4.0. It has 49,600 parameters. At 16-bit it needs about 0 GB of GPU memory, which fits on 1x MI300X from $1.85 an hour, at the lowest prices in the SAVRN Index.
Runs On
What it takes to serve contrastive-learning-2023 (49,600 parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.
| Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits |
|---|---|---|---|---|---|
| 16-bit | 0.0 GB | 0.0 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 8-bit | 0.0 GB | 0.0 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 4-bit | 0.0 GB | 0.0 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Oct 20, 2026.
contrastive-learning-2023 on every accelerator the SAVRN Index prices, at every precision
Model Card
Identity and Version
- Repository
- patelnikita/contrastive-learning-2023
- Publisher
- Patelnikita
- Task
- Not stated by the source
- Modality
- Other
- Library
- Not stated by the source
- Parameters
- 49,600 parameters
- Languages
- Not stated by the source
- Revision
- ce21f9adb29e6d2848cf8ee9a25ea2191e74f0ba
- First published
- 2026-10-20
- Last updated
- 2026-10-20
License and Download
- License
- cc-by-4.0
- Access
- Open weights, no gate
Released by Patelnikita through its official repository on Hugging Face. Read the license.
Memory Requirements
| Precision | Weights in memory |
|---|---|
| 16-bit | 0.0 GB |
| 8-bit | 0.0 GB |
| 4-bit | 0.0 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About contrastive-learning-2023
How much GPU memory does contrastive-learning-2023 need?
About 0 GB at 16-bit and 0 GB at 4-bit: the weights (49,600 parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run contrastive-learning-2023 on?
At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.
Can I use contrastive-learning-2023 commercially?
Yes. contrastive-learning-2023 is released under Creative Commons Attribution 4.0. CC BY 4.0 permits sharing and adapting the work, including commercially, provided the creator is credited and changes are indicated.