# MOMENT-1-small by Auton Lab: Open-Weight Model Source: https://savrn.com/models/moment-1-small Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## Runs On What it takes to serve MOMENT-1-small (38M 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. [MOMENT-1-small on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/moment-1-small/gpus) ## Model Card By Auton Lab, published under mit, revision 411e288267f8. ### MOMENT-Small 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](https://savrn.com/papers/moment-a-family-of-open-time-series-foundation-models). MOMENT-1 comes in 3 sizes: [Small](https://savrn.com/models/moment-1-small), [Base](https://savrn.com/models/moment-1-base), and [Large](https://savrn.com/models/moment-1-large). ### Usage Recommended Python Version: Python 3.11 (support for additional versions is expected soon). You can install the momentfm package using pip: ``` pip install momentfm ``` Alternatively, to install the latest version directly from the GitHub repository: ``` pip install git+https://github.com/moment-timeseries-foundation-model/moment.git ``` To load the pre-trained model for one of the tasks, use one of the following code snippets: Forecasting [Read the full model card (577 words)](https://savrn.com/models/moment-1-small/card) ## Identity and Version Repository AutonLab/MOMENT-1-small Publisher Auton Lab Task Time series forecasting Modality Time series Library transformers Parameters 38M parameters Languages Not stated by the source Revision 411e288267f82cce86296dbe4d6c8bc533cc162f First published 2024-10-10 Last updated 2025-03-26 ## Files and Weights 5 files, 303.3 MB in total. The weights are 2 files totalling 303.3 MB in bin, safetensors. Weights2 files · 303.3 MB Configuration1 file · 947 B Documentation1 file · 6.3 KB Repository1 file · 1.5 KB Every file | File | Type | Size | SHA-256 | | --- | --- | --- | --- | | model.safetensors | Weights | 151.6 MB | 785e6c6f57ff | | pytorch_model.bin | Weights | 151.6 MB | 90c566d82684 | | config.json | Configuration | 947 B | — | | README.md | Documentation | 6.3 KB | — | | .gitattributes | Repository | 1.5 KB | — | ## License and Download License mit Access Open weights, no gate Download size 303.3 MB [Download from Auton Lab](https://huggingface.co/AutonLab/MOMENT-1-small) Released by Auton Lab through its official repository on Hugging Face. [Read the license](https://opensource.org/license/mit). ## Built From - Described by [arXiv:2402.03885](https://savrn.com/papers/moment-a-family-of-open-time-series-foundation-models) - Trained on (disclosed) AutonLab/Timeseries-PILE ## Memory Requirements | Precision | Weights in memory | | --- | --- | | As published | 303.3 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 MOMENT-1-small - [chronos-bolt-small vs MOMENT-1-small](https://savrn.com/models/compare/chronos-bolt-small-2-vs-moment-1-small) - [chronos-bolt-small vs MOMENT-1-small](https://savrn.com/models/compare/chronos-bolt-small-vs-moment-1-small) ## Questions About MOMENT-1-small ### How much GPU memory does MOMENT-1-small need? About 0.1 GB at 16-bit and 0 GB at 4-bit: the weights (38M parameters) plus a working margin. A long context needs more. ### What is the cheapest GPU to run MOMENT-1-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 MOMENT-1-small commercially? Yes. MOMENT-1-small is released under MIT License. The MIT License is a short permissive license. It permits commercial use, modification and redistribution, provided the copyright notice and permission notice are included. ## Similar Models Model · Time series forecasting ### [chronos-t5-small](https://savrn.com/models/chronos-t5-small) [Amazon](https://savrn.com/model-publishers/amazon) 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 chronos-forecasting [View model](https://savrn.com/models/chronos-t5-small) 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 ### [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) ## Auton Lab [All models and datasets](https://savrn.com/model-publishers/autonlab) ## Versions - [411e288267f8](https://savrn.com/models/moment-1-small/versions/411e288267f8) · current 2026-09-18 ## Explore More - [All time series forecasting models](https://savrn.com/models/tasks/time-series-forecasting) - [All models under mit](https://savrn.com/models/licenses/mit) - [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 2026-10-20. - [Hugging Face record](https://huggingface.co/AutonLab/MOMENT-1-small) - [How the hub is built](https://savrn.com/model-hub/methodology)