# MOMENT-1-base by Auton Lab: Open-Weight Model Source: https://savrn.com/models/moment-1-base Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## Runs On What it takes to serve MOMENT-1-base (113M 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 | 0.2 GB | 0.3 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 | 0.1 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 | | 4-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 | 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. [MOMENT-1-base on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/moment-1-base/gpus) ## Model Card By Auton Lab, published under mit, revision 5e44b0ea2637. ### MOMENT-base 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-base/card) ## Identity and Version Repository AutonLab/MOMENT-1-base Publisher Auton Lab Task Time series forecasting Modality Time series Library transformers Parameters 113M parameters Languages Not stated by the source Revision 5e44b0ea26376a176360f87831124e018f876d96 First published 2024-10-12 Last updated 2025-03-26 ## Files and Weights 5 files, 907.9 MB in total. The weights are 2 files totalling 907.9 MB in bin, safetensors. Weights2 files · 907.9 MB Configuration1 file · 949 B Documentation1 file · 6.3 KB Repository1 file · 1.5 KB Every file | File | Type | Size | SHA-256 | | --- | --- | --- | --- | | model.safetensors | Weights | 453.9 MB | 1a436826ffe6 | | pytorch_model.bin | Weights | 454.0 MB | 23c3d65bbb6d | | config.json | Configuration | 949 B | — | | README.md | Documentation | 6.3 KB | — | | .gitattributes | Repository | 1.5 KB | — | ## License and Download License mit Access Open weights, no gate Download size 907.9 MB [Download from Auton Lab](https://huggingface.co/AutonLab/MOMENT-1-base) 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 | 907.9 MB | | 16-bit | 0.2 GB | | 8-bit | 0.1 GB | | 4-bit | 0.1 GB | Weights only, from the published parameter count; the key-value cache and runtime add to this. ## Compare MOMENT-1-base - [chronos-2 vs MOMENT-1-base](https://savrn.com/models/compare/chronos-2-2-vs-moment-1-base) - [chronos-2 vs MOMENT-1-base](https://savrn.com/models/compare/chronos-2-vs-moment-1-base) ## Questions About MOMENT-1-base ### How much GPU memory does MOMENT-1-base need? About 0.3 GB at 16-bit and 0.1 GB at 4-bit: the weights (113M parameters) plus a working margin. A long context needs more. ### What is the cheapest GPU to run MOMENT-1-base 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-base commercially? Yes. MOMENT-1-base 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 ### [TimeMoE-50M](https://savrn.com/models/timemoe-50m) [Xiaoming Shi](https://savrn.com/model-publishers/maple728) This repository contains the weights of the TimeMoE-50M model of the paper Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts. For details on how to use this model, please visit our GitHub page. Open weights apache-2.0 113M parameters 4,096 tokens [View model](https://savrn.com/models/timemoe-50m) Model · Time series forecasting ### [moirai-moe-1.0-R-small](https://savrn.com/models/moirai-moe-1-0-r-small) [Salesforce AI Research](https://savrn.com/model-publishers/salesforce) This model has been pushed to the Hub using the PytorchModelHubMixin integration: This release is for research purposes only in support of an academic paper. Our models, datasets, and code are not specifically designed or evaluated for all downstream purposes. We strongly recommend users evaluate and address potential concerns related to accuracy, safety, and fairness before deploying this model. We encourage users to consider the common limitations of AI, comply with applicable laws, and leverage best practices when selecting use cases, particularly for high-risk scenarios where errors or misuse could significantly impact people’s lives, rights, or safety. For further guidance on use… Open weights cc-by-nc-4.0 117M parameters [View model](https://savrn.com/models/moirai-moe-1-0-r-small) Model · Time series forecasting ### [chronos-2-synth](https://savrn.com/models/chronos-2-synth) [Autogluon](https://savrn.com/model-publishers/autogluon) This is a variant of the Chronos-2 model which has only been trained on synthetic univariate and multivariate data. 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 119M parameters chronos-forecasting [View model](https://savrn.com/models/chronos-2-synth) Model · Time series forecasting ### [chronos-2](https://savrn.com/models/chronos-2) [Amazon](https://savrn.com/model-publishers/amazon) Update Jun 5, 2026: Deploy Chronos-2 on AWS with AutoGluon-Cloud. Real-time, serverless, or batch inference in 3 lines of code — pandas DataFrames in, forecasts out. Check out the new deployment guide. Chronos-2 is a 120M-parameter, encoder-only time series foundation model for zero-shot forecasting. It supports univariate, multivariate, and covariate-informed tasks within a single architecture. Inspired by the T5 encoder, Chronos-2 produces multi-step-ahead quantile forecasts and uses a group attention mechanism for efficient in-context learning across related series and covariates. Trained on a combination of real-world and large-scale synthetic datasets, it achieves state-of-the-art… Open weights apache-2.0 119M parameters chronos-forecasting [View model](https://savrn.com/models/chronos-2) Model · Time series forecasting ### [chronos-2](https://savrn.com/models/chronos-2-2) [Autogluon](https://savrn.com/model-publishers/autogluon) Update Jun 5, 2026: Deploy Chronos-2 on AWS with AutoGluon-Cloud. Real-time, serverless, or batch inference in 3 lines of code — pandas DataFrames in, forecasts out. Check out the new deployment guide. Chronos-2 is a 120M-parameter, encoder-only time series foundation model for zero-shot forecasting. It supports univariate, multivariate, and covariate-informed tasks within a single architecture. Inspired by the T5 encoder, Chronos-2 produces multi-step-ahead quantile forecasts and uses a group attention mechanism for efficient in-context learning across related series and covariates. Trained on a combination of real-world and large-scale synthetic datasets, it achieves state-of-the-art… Open weights apache-2.0 119M parameters chronos-forecasting [View model](https://savrn.com/models/chronos-2-2) Model · Time series forecasting ### [Kronos-base](https://savrn.com/models/kronos-base) [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 102M parameters [View model](https://savrn.com/models/kronos-base) ## Auton Lab [All models and datasets](https://savrn.com/model-publishers/autonlab) ## Versions - [5e44b0ea2637](https://savrn.com/models/moment-1-base/versions/5e44b0ea2637) · 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-base) - [How the hub is built](https://savrn.com/model-hub/methodology)