# mivolo_v2 by Irina Tolstykh: Open-Weight Model Source: https://savrn.com/models/mivolo-v2 Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## Runs On What it takes to serve mivolo_v2 (29M 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 | 0.1 GB | 0.1 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | | 8-bit | 0.0 GB | 0.0 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | | 4-bit | 0.0 GB | 0.0 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/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](https://savrn.com/ai-index/pricing/gpus), read Oct 25, 2026. [mivolo_v2 on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/mivolo-v2/gpus) ## SAVRN's Notes on mivolo_v2 Point it at a photo and it returns an age estimate and a gender label — the entire job for this 29M-parameter multi-input transformer. Weights are 115.1 MB stored in float16; the 16-bit row reads 0.1 GB and the 8-bit and 4-bit rows round to zero, so the cheapest listed setup, a 192 GB MI300X at $1.85 an hour, is theoretical. You batch images against a shared card or run it on CPU-adjacent hardware; 1.8M monthly downloads say pipelines do exactly that. Apache 2.0 permits commercial use, modification and redistribution, with notices kept, changes stated and an express patent grant. The pinned stack is the mivolo library — the repo ships its own modeling and preprocessing code — so version-lock your serving environment; the repo was last updated 2026-03-11. And do your own bias evaluation before shipping age or gender inference in a product: the card describes the architecture, not its error modes, and does not name its training data. SAVRN Research ## Model Card By Irina Tolstykh, published under apache-2.0, revision 53393526c220. ### Model Card for MiVOLO V2 model [Read the full model card (318 words)](https://savrn.com/models/mivolo-v2/card) ## Configuration Architecture MiVOLOForImageClassification Stored precision float16 Model type mivolo ## Identity and Version Repository iitolstykh/mivolo_v2 Publisher Irina Tolstykh Task Not stated by the source Modality Other Library mivolo Parameters 29M parameters Languages Not stated by the source Revision 53393526c220e34cdd7b722b36d22b6f9e5f4241 First published 2024-03-05 Last updated 2026-03-11 ## Files and Weights 10 files, 115.1 MB in total. The weights are 1 file totalling 115.1 MB in safetensors. Weights1 file · 115.1 MB Configuration5 files · 9.2 KB Documentation1 file · 3.8 KB Other2 files · 35.4 KB Repository1 file · 1.6 KB Every file | File | Type | Size | SHA-256 | | --- | --- | --- | --- | | model.safetensors | Weights | 115.1 MB | 96efb47051c0 | | config.json | Configuration | 924 B | — | | configuration_mivolo.py | Configuration | 1.5 KB | — | | mivolo_image_processor.py | Configuration | 1.5 KB | — | | modeling_mivolo.py | Configuration | 5.3 KB | — | | preprocessor_config.json | Configuration | 96 B | — | | README.md | Documentation | 3.8 KB | — | | icon.jpg | Other | 35.3 KB | — | | requirements.txt | Other | 150 B | — | | .gitattributes | Repository | 1.6 KB | — | ## License and Download License apache-2.0 Access Open weights, no gate Download size 115.1 MB [Download from Irina Tolstykh](https://huggingface.co/iitolstykh/mivolo_v2) Released by Irina Tolstykh through its official repository on Hugging Face. [Read the license](https://www.apache.org/licenses/LICENSE-2.0). ## Built From - Described by [arXiv:2307.04616](https://savrn.com/papers/mivolo-multi-input-transformer-for-age-and-gender-estimation) - Described by [arXiv:2403.02302](https://savrn.com/papers/beyond-specialization-assessing-the-capabilities-of-mllms-in-age-and-gender-estimation) ## Memory Requirements | Precision | Weights in memory | | --- | --- | | As published | 115.1 MB | | 16-bit | 0.1 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 mivolo_v2 ### How much GPU memory does mivolo_v2 need? About 0.1 GB at 16-bit and 0 GB at 4-bit: the weights (29M parameters) plus a working margin. A long context needs more. ### What is the cheapest GPU to run mivolo_v2 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 mivolo_v2 commercially? Yes. mivolo_v2 is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors. ## Irina Tolstykh [All models and datasets](https://savrn.com/model-publishers/iitolstykh) ## Versions - [53393526c220](https://savrn.com/models/mivolo-v2/versions/53393526c220) · current 2026-10-25 ## Explore More - [All models under apache-2.0](https://savrn.com/models/licenses/apache-2-0) - [Model comparisons](https://savrn.com/models/comparisons) - [The model directory](https://savrn.com/models) - [Open model prices by host](https://savrn.com/ai-index/pricing/open-models) ## Source - Repository metadata, read 2026-10-19. - [Hugging Face record](https://huggingface.co/iitolstykh/mivolo_v2) - [How the hub is built](https://savrn.com/model-hub/methodology)