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Open-weight model · Time series forecasting
by Salesforce AI Research Salesforce/moirai-moe-1.0-R-small
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.
What it takes to serve moirai-moe-1.0-R-small (117M 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 Sep 18, 2026.
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…
Excerpt from the card by Salesforce AI Research, licensed cc-by-nc-4.0.
4 files, 468.8 MB in total. The weights are 1 file totalling 468.8 MB in safetensors.
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 468.8 MB | da1ecb289814 |
| config.json | Configuration | 665 B | — |
| README.md | Documentation | 1.1 KB | — |
| .gitattributes | Repository | 1.5 KB | — |
Released by Salesforce AI Research through its official repository on Hugging Face. Read the license.
| Precision | Weights in memory |
|---|---|
| As published | 468.8 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.
About 0.3 GB at 16-bit and 0.1 GB at 4-bit: the weights (117M parameters) plus a working margin. A long context needs more.
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.
Not without separate permission. moirai-moe-1.0-R-small is released under Creative Commons Attribution-NonCommercial 4.0. CC BY-NC 4.0 permits sharing and adapting with credit for non-commercial purposes only. Commercial use needs separate permission from the rights holder.
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
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…
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…
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…
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.
News (2025.08) Sundial has been integrated into Apache IoTDB, a native time-series database. News (2025.06) Sundial has been accepted as ICML 2025 Oral (Top 1%). News (2025.05) Get 1st MASE on the GIFT-Eval Benchmark. News (2025.02) Get 1st MSE/MAE zero-shot performance on Time-Series-Library datasets. Sundial is a family of generative time series foundation models. This version is pre-trained on 1 trillion time points with 128M parameters. For more information, please refer to this paper. [[Slides]](https://cloud.tsinghua.edu.cn/f/8d526337afde465e87c9/) [[Poster]](https://cloud.tsinghua.edu.cn/f/cc2a156315e9453f99b3/) [[Intro (CN)]](https://mp.weixin.qq.com/s/y3sc2e2lmW1sqfnoK-ZdDA). The…
