# chronos-2-small by Autogluon: Open-Weight Model Source: https://savrn.com/models/chronos-2-small Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## Runs On What it takes to serve chronos-2-small (28M 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. [chronos-2-small on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/chronos-2-small/gpus) ## SAVRN's Notes on chronos-2-small There is one job here, forecasting a numeric series, and it makes the hardware question nearly disappear. With 28M parameters, Chronos-2-small needs 0.1 GB at 16-bit. The cheapest setup on this page is one MI300X with 192 GB at $1.85 an hour, so nobody should rent a card for this model alone. We would place it beside something larger and treat its memory as a rounding error. Nothing in the license slows a deployment: Apache 2.0 permits commercial use, modification and redistribution, with an express patent grant, and asks that the notices travel with it and significant changes be stated. Two checks. No context length is listed, so settle the history window with the publisher's documentation. And the training relations name autogluon/chronos_datasets and Salesforce/GiftEvalPretrain; if your series overlap with either, your test numbers will flatter it. No evaluations are reported here, so run your own. SAVRN Research, 2026-09-18 ## Model Card By Autogluon, published under apache-2.0, revision ddec01313e50. This is the small variant of the Chronos-2 model with 28M parameters. For usage and details on the Chronos-2 model, please refer to https://huggingface.co/autogluon/chronos-2. If you find Chronos-2 useful for your research, please consider citing the associated paper Read Autogluon's full model card This is the small variant of the Chronos-2 model with 28M parameters. For usage and details on the Chronos-2 model, please refer to https://huggingface.co/autogluon/chronos-2. ### Citation If you find Chronos-2 useful for your research, please consider citing the associated paper: ``` @article{ansari2025chronos2, title = {Chronos-2: From Univariate to Universal Forecasting}, author = {Abdul Fatir Ansari and Oleksandr Shchur and Jaris Küken and Andreas Auer and Boran Han and Pedro Mercado and Syama Sundar Rangapuram and Huibin Shen and Lorenzo Stella and Xiyuan Zhang and Mononito Goswami and Shubham Kapoor and Danielle C. Maddix and Pablo Guerron and Tony Hu and Junming Yin and Nick Erickson and Prateek Mutalik Desai and Hao Wang and Huzefa Rangwala and George Karypis and Yuyang Wang and Michael Bohlke-Schneider}, year = {2025}, url = {https://arxiv.org/abs/2510.15821} } ``` ## Configuration Architecture Chronos2Model Vocabulary size 2 RoPE base 10000 Stored precision float32 Model type t5 ## Identity and Version Repository autogluon/chronos-2-small Publisher Autogluon Task Time series forecasting Modality Time series Library chronos-forecasting Parameters 28M parameters Languages Not stated by the source Revision ddec01313e50b6bc58ebaa92ede81bc24a3d9f9a First published 2025-12-03 Last updated 2025-12-03 ## Files and Weights 4 files, 111.8 MB in total. The weights are 1 file totalling 111.7 MB in safetensors. Weights1 file · 111.7 MB Configuration1 file · 969 B Documentation1 file · 1.4 KB Repository1 file · 1.5 KB Every file | File | Type | Size | SHA-256 | | --- | --- | --- | --- | | model.safetensors | Weights | 111.7 MB | 492290ae82bb | | config.json | Configuration | 969 B | — | | README.md | Documentation | 1.4 KB | — | | .gitattributes | Repository | 1.5 KB | — | ## License and Download License apache-2.0 Access Open weights, no gate Download size 111.7 MB [Download from Autogluon](https://huggingface.co/autogluon/chronos-2-small) Released by Autogluon through its official repository on Hugging Face. [Read the license](https://www.apache.org/licenses/LICENSE-2.0). ## Built From - Described by [arXiv:2510.15821](https://savrn.com/papers/chronos-2-from-univariate-to-universal-forecasting) - Trained on (disclosed) [Salesforce/GiftEvalPretrain](https://savrn.com/datasets/giftevalpretrain) - Trained on (disclosed) autogluon/chronos_datasets ## Memory Requirements | Precision | Weights in memory | | --- | --- | | As published | 111.7 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. ## Compare chronos-2-small - [chronos-2-small vs chronos-bolt-small](https://savrn.com/models/compare/chronos-2-small-vs-chronos-bolt-small) - [chronos-2-small vs Kronos-small](https://savrn.com/models/compare/chronos-2-small-vs-kronos-small) - [chronos-2-small vs Toto-2.0-22m](https://savrn.com/models/compare/chronos-2-small-vs-toto-2-0-22m) ## Questions About chronos-2-small ### How much GPU memory does chronos-2-small need? About 0.1 GB at 16-bit and 0 GB at 4-bit: the weights (28M parameters) plus a working margin. A long context needs more. ### What is the cheapest GPU to run chronos-2-small 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 chronos-2-small commercially? Yes. chronos-2-small 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. ## Similar Models Model · Time series forecasting ### [Kronos-small](https://savrn.com/models/kronos-small) [ShiYu](https://savrn.com/model-publishers/neoquasar) Kronos is the first open-source foundation model for financial candlesticks (K-lines), trained on data from over 45 global exchanges. 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Check out the tutorial notebook to learn how to deploy Chronos endpoints for production use in a few lines of code. Chronos-Bolt is a family of pretrained time series forecasting models which can be used for zero-shot forecasting. It is based on the T5 encoder-decoder architecture and has been trained on nearly 100 billion time series observations. It chunks the historical time series context into patches of multiple observations, which are then input into the encoder. The decoder then uses these representations to directly generate quantile forecasts across multiple future steps—a method known as… Open weights apache-2.0 21M parameters [View model](https://savrn.com/models/chronos-bolt-mini) Model · Time series forecasting ### [chronos-bolt-mini](https://savrn.com/models/chronos-bolt-mini-2) [Amazon](https://savrn.com/model-publishers/amazon) Update Feb 14, 2025: Chronos-Bolt models are now available on Amazon SageMaker JumpStart! Check out the tutorial notebook to learn how to deploy Chronos endpoints for production use in a few lines of code. Chronos-Bolt is a family of pretrained time series forecasting models which can be used for zero-shot forecasting. It is based on the T5 encoder-decoder architecture and has been trained on nearly 100 billion time series observations. It chunks the historical time series context into patches of multiple observations, which are then input into the encoder. The decoder then uses these representations to directly generate quantile forecasts across multiple future steps—a method known as… Open weights apache-2.0 21M parameters chronos-forecasting [View model](https://savrn.com/models/chronos-bolt-mini-2) Model · Time series forecasting ### [chronos-t5-mini](https://savrn.com/models/chronos-t5-mini) [Autogluon](https://savrn.com/model-publishers/autogluon) Update Feb 14, 2025: Chronos-Bolt & original Chronos models are now available on Amazon SageMaker JumpStart! 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Check out the tutorial notebook to learn how to deploy Chronos endpoints for production use in a few lines of code. Update Nov 27, 2024: We have released Chronos-Bolt models that are more accurate (5% lower error), up to 250 times faster and 20 times more memory-efficient than the original Chronos models of the same size. Check out the new models here. Chronos is a family of pretrained time series forecasting models based on language model architectures. A time series is transformed into a sequence of tokens via scaling and quantization, and a language model is trained on these… Open weights apache-2.0 20M parameters chronos-forecasting [View model](https://savrn.com/models/chronos-t5-mini-2) ## Autogluon [All models and datasets](https://savrn.com/model-publishers/autogluon) ## Versions - [ddec01313e50](https://savrn.com/models/chronos-2-small/versions/ddec01313e50) · current 2026-10-25 ## Explore More - [All time series forecasting models](https://savrn.com/models/tasks/time-series-forecasting) - [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. - [Autogluon website](https://auto.gluon.ai/) - [Hugging Face record](https://huggingface.co/autogluon/chronos-2-small) - [How the hub is built](https://savrn.com/model-hub/methodology)