# repvgg_a0.rvgg_in1k by PyTorch Image Models: Open Model Source: https://savrn.com/models/repvgg-a0-rvgg-in1k Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## Runs On What it takes to serve repvgg_a0.rvgg_in1k (9M 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](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 23, 2026. [repvgg_a0.rvgg_in1k on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/repvgg-a0-rvgg-in1k/gpus) ## SAVRN's Notes on repvgg_a0.rvgg_in1k RepVGG is what you deploy when you want a plain VGG-style convnet with modern accuracy: this a0 checkpoint is 9M parameters, 1.5 GMACs at 224 pixels, and 73.2 MB on disk. Weights and working memory both round to 0.0 GB at every precision, so the only real question is images per second on hardware you already own; a dedicated MI300X at $1.85 an hour makes no sense for it. MIT covers commercial use, modification and redistribution with a notice obligation. The checkpoint was trained on ImageNet-1k by the paper authors and is implemented in timm's BYOBNet, which also gives per-stage feature extraction if you want a backbone rather than a classifier. Anything outside the ImageNet-1k label set means a fine-tune. The page cites arXiv:2101.03697, first published 2023-08-23, last updated 2025-01-21, with 597.3k downloads a month. SAVRN Research ## Model Card By PyTorch Image Models, published under mit, revision e292d220aa8b. ### Model card for repvgg_a0 A RepVGG image classification model. Trained on ImageNet-1k by paper authors. This model architecture is implemented using timm's flexible [BYOBNet (Bring-Your-Own-Blocks Network)](https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/byobnet.py). BYOBNet allows configuration of: * block / stage layout * stem layout * output stride (dilation) * activation and norm layers * channel and spatial / self-attention layers ...and also includes timm features common to many other architectures, including: * stochastic depth * gradient checkpointing * layer-wise LR decay * per-stage feature extraction ### Model Details - Model Type: Image classification / feature backbone - Model Stats: - Params (M): 9.1 - GMACs: 1.5 - Activations (M): 3.6 - Image size: 224 x 224 - Papers: - RepVGG: Making VGG-style ConvNets Great Again: https://arxiv.org/abs/2101.03697 - Dataset: ImageNet-1k - Original: https://github.com/DingXiaoH/RepVGG ### Model Usage #### Image Classification [Read the full model card (440 words)](https://savrn.com/models/repvgg-a0-rvgg-in1k/card) ## Identity and Version Repository timm/repvgg_a0.rvgg_in1k Publisher PyTorch Image Models Task Image classification Modality Image Library timm Parameters 9M parameters Languages Not stated by the source Revision e292d220aa8b811232037f8aa6d6c8c552dbd0c0 First published 2023-08-23 Last updated 2025-01-21 ## Files and Weights 5 files, 73.2 MB in total. The weights are 2 files totalling 73.2 MB in bin, safetensors. Weights2 files · 73.2 MB Configuration1 file · 645 B Documentation1 file · 4.5 KB Repository1 file · 1.5 KB Every file | File | Type | Size | SHA-256 | | --- | --- | --- | --- | | model.safetensors | Weights | 36.6 MB | 2b2bfd7feff2 | | pytorch_model.bin | Weights | 36.7 MB | d8d1275b0e8a | | config.json | Configuration | 645 B | — | | README.md | Documentation | 4.5 KB | — | | .gitattributes | Repository | 1.5 KB | — | ## License and Download License mit Access Open weights, no gate Download size 73.2 MB [Download from PyTorch Image Models](https://huggingface.co/timm/repvgg_a0.rvgg_in1k) Released by PyTorch Image Models through its official repository on Hugging Face. [Read the license](https://opensource.org/license/mit). ## Built From - Described by arXiv:2101.03697 - Trained on (disclosed) imagenet-1k ## Memory Requirements | Precision | Weights in memory | | --- | --- | | As published | 73.2 MB | | 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 repvgg_a0.rvgg_in1k ### How much GPU memory does repvgg_a0.rvgg_in1k need? About 0 GB at 16-bit and 0 GB at 4-bit: the weights (9M parameters) plus a working margin. A long context needs more. ### What is the cheapest GPU to run repvgg_a0.rvgg_in1k 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 repvgg_a0.rvgg_in1k commercially? Yes. repvgg_a0.rvgg_in1k is released under MIT License. The MIT License is a short permissive license. 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