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.
MOMENT-1-base is an open-weight model for time series forecasting from Auton Lab, released under MIT License. It has 113M parameters. At 16-bit it needs about 0.3 GB of GPU memory, which fits on 1x MI300X from $1.85 an hour, at the lowest prices in the SAVRN Index. It draws 44.6k downloads a month.
MOMENT is a family of foundation models for general-purpose time-series analysis.
Parameters113M
Context—
Weights907.9 MB
Licensemit
AccessOpen weights
Monthly Downloads44.6k
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 (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 8-bit | 0.1 GB | 0.1 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 4-bit | 0.1 GB | 0.1 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x 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, read Oct 16, 2026.
MOMENT-1-base on every accelerator the SAVRN Index prices, at every precision
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.
MOMENT-1 comes in 3 sizes: Small, Base, and 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
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
Released by Auton Lab through its official repository on Hugging Face. Read the license.
Built From
- Described by arXiv:2402.03885
- 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
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.
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