# chronos-t5-small by Amazon: Open-Weight Model Source: https://savrn.com/models/chronos-t5-small Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## Runs On What it takes to serve chronos-t5-small (46M 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 | 0.1 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 | | 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. [chronos-t5-small on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/chronos-t5-small/gpus) ## SAVRN's Notes on chronos-t5-small Amazon's own notes point past this model. Chronos-t5-small is the original Chronos, 46M parameters on a T5 architecture, and the publisher says the Chronos-Bolt line released November 27, 2024 runs with 5% lower error, up to 250 times faster and 20 times more memory-efficient at the same size. The original forecasts time series by turning the values into tokens, with a vocabulary of 4,096. Memory is 0.1 GB at 16-bit, and the cheapest host we list, one MI300X at $1.85 an hour, is oversized for it many times over. We would run it wherever a load-forecast service already lives. Under Apache 2.0 a commercial deployment and modifications are fine, with notices kept. Check two things: no context length is recorded, so confirm the history window in the chronos-forecasting library before sizing inputs, and read the Chronos paper, arXiv:2403.07815, against Bolt before choosing. SAVRN Research, 2026-09-18 ## Model Card By Amazon, published under apache-2.0, revision a971ba21945c. Update Feb 14, 2025: Chronos-Bolt & original Chronos models are now available on Amazon SageMaker JumpStart! Check out the [tutorial notebook](https://github.com/amazon-science/chronos-forecasting/blob/main/notebooks/deploy-chronos-to-amazon-sagemaker.ipynb) 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](https://savrn.com/models/chronos-bolt-small). [Read the full model card (570 words)](https://savrn.com/models/chronos-t5-small/card) ## Configuration Architecture T5ForConditionalGeneration Vocabulary size 4,096 Stored precision float32 Model type t5 ## Identity and Version Repository amazon/chronos-t5-small Publisher Amazon Task Time series forecasting Modality Time series Library chronos-forecasting Parameters 46M parameters Languages Not stated by the source Revision a971ba21945c4f1796b17a91fe69214b5f4ad472 First published 2024-02-21 Last updated 2025-11-21 ## Files and Weights 6 files, 184.9 MB in total. The weights are 1 file totalling 184.6 MB in safetensors. Weights1 file · 184.6 MB Configuration2 files · 1.3 KB Documentation1 file · 6.1 KB Other1 file · 232.3 KB Repository1 file · 1.5 KB Every file | File | Type | Size | SHA-256 | | --- | --- | --- | --- | | model.safetensors | Weights | 184.6 MB | 9c8b6fde5300 | | config.json | Configuration | 1.1 KB | — | | generation_config.json | Configuration | 142 B | — | | README.md | Documentation | 6.1 KB | — | | figures/main-figure.png | Other | 232.3 KB | — | | .gitattributes | Repository | 1.5 KB | — | ## License and Download License apache-2.0 Access Open weights, no gate Download size 184.6 MB [Download from Amazon](https://huggingface.co/amazon/chronos-t5-small) Released by Amazon through its official repository on Hugging Face. [Read the license](https://www.apache.org/licenses/LICENSE-2.0). ## Built From - Described by [arXiv:1910.10683](https://savrn.com/papers/exploring-the-limits-of-transfer-learning-with-a-unified-text-to-text-transformer) - Described by [arXiv:2403.07815](https://savrn.com/papers/chronos-learning-the-language-of-time-series) ## Memory Requirements | Precision | Weights in memory | | --- | --- | | As published | 184.6 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-t5-small - [chronos-bolt-small vs chronos-t5-small](https://savrn.com/models/compare/chronos-bolt-small-2-vs-chronos-t5-small) - [chronos-bolt-small vs chronos-t5-small](https://savrn.com/models/compare/chronos-bolt-small-vs-chronos-t5-small) ## Questions About chronos-t5-small ### How much GPU memory does chronos-t5-small need? About 0.1 GB at 16-bit and 0 GB at 4-bit: the weights (46M parameters) plus a working margin. A long context needs more. ### What is the cheapest GPU to run chronos-t5-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-t5-small commercially? Yes. chronos-t5-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 ### [chronos-t5-small](https://savrn.com/models/chronos-t5-small-2) [Autogluon](https://savrn.com/model-publishers/autogluon) Update Feb 14, 2025: Chronos-Bolt & original Chronos 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. 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 46M parameters transformers [View model](https://savrn.com/models/chronos-t5-small-2) Model · Time series forecasting ### [chronos-bolt-small](https://savrn.com/models/chronos-bolt-small) [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 48M parameters chronos-forecasting [View model](https://savrn.com/models/chronos-bolt-small) Model · Time series forecasting ### [chronos-bolt-small](https://savrn.com/models/chronos-bolt-small-2) [Autogluon](https://savrn.com/model-publishers/autogluon) 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 48M parameters [View model](https://savrn.com/models/chronos-bolt-small-2) Model · Time series forecasting ### [MOMENT-1-small](https://savrn.com/models/moment-1-small) [Auton Lab](https://savrn.com/model-publishers/autonlab) MOMENT is a family of foundation models for general-purpose time-series analysis. The models in this family (1) serve as a building block for diverse time-series analysis tasks (e.g., forecasting, classification, anomaly detection, and imputation, etc.), (2) are effective out-of-the-box, i.e., with no (or few) task-specific exemplars (enabling e.g., zero-shot forecasting, few-shot classification, etc.), and (3) are tunable using in-distribution and task-specific data to improve performance. For details on MOMENT models, training data, and experimental results, please refer to the paper MOMENT: A Family of Open Time-series Foundation Models. Recommended Python Version: Python 3.11 (support… Open weights mit 38M parameters transformers [View model](https://savrn.com/models/moment-1-small) Model · Time series forecasting ### [chronos-2-small](https://savrn.com/models/chronos-2-small) [Autogluon](https://savrn.com/model-publishers/autogluon) 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 Open weights apache-2.0 28M parameters chronos-forecasting [View model](https://savrn.com/models/chronos-2-small) 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. It is designed to handle the unique, high-noise characteristics of financial data. Kronos is a family of decoder-only foundation models, pre-trained specifically for the "language" of financial markets—K-line sequences. It leverages a novel two-stage framework: 1. A specialized tokenizer first quantizes continuous, multi-dimensional K-line data (OHLCV) into hierarchical discrete tokens. 2. A large, autoregressive Transformer is then pre-trained on these tokens, enabling it to serve as a unified model for diverse quantitative tasks. The success of large-scale… Open weights mit 25M parameters [View model](https://savrn.com/models/kronos-small) ## Amazon [All models and datasets](https://savrn.com/model-publishers/amazon) ## Versions - [a971ba21945c](https://savrn.com/models/chronos-t5-small/versions/a971ba21945c) · current 2026-10-18 ## 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. - [Amazon website](https://github.com/amazon-science) - [Hugging Face record](https://huggingface.co/amazon/chronos-t5-small) - [How the hub is built](https://savrn.com/model-hub/methodology)