# Toto-Open-Base-1.0 by Datadog: Open-Weight Model Source: https://savrn.com/models/toto-open-base-1-0 Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## Runs On What it takes to serve Toto-Open-Base-1.0 (151M 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 | 0.3 GB | 0.4 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 | 0.2 GB | 0.2 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. [Toto-Open-Base-1.0 on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/toto-open-base-1-0/gpus) ## SAVRN's Notes on Toto-Open-Base-1.0 Toto Open Base 1.0 was Datadog's first open forecasting model, 151M parameters trained heavily on observability data, and it handles high-dimensional series with many related variables. Toto 2.0 in our catalog is the newer generation. Apache 2.0 allows commercial use. For new work, test the Toto 2.0 models first; they are smaller and newer. Keep 1.0 where results were validated on it. SAVRN Research, 2026-09-18 ## Model Card By Datadog, published under apache-2.0, revision 0411ceb27bdf. Toto (Time Series Optimized Transformer for [Observability](https://www.datadoghq.com/knowledge-center/observability/)) is a state-of-the-art time-series foundation model designed for multi-variate time series forecasting, emphasizing observability metrics. Toto efficiently handles high-dimensional, sparse, and non-stationary data commonly encountered in observability scenarios. The average rank of Toto compared to the runner-up models on both the [GIFT-Eval](https://huggingface.co/spaces/Salesforce/GIFT-Eval) and [BOOM](https://huggingface.co/datasets/Datadog/BOOM) benchmarks (as of May 19, 2025). ### Key Features - Zero-Shot Forecasting: Perform forecasting without fine-tuning on your specific time series. - High-Dimension Multi-Variate Support: Efficiently process multiple variables using Proportional Factorized Space-Time Attention. - Decoder-Only Transformer Architecture: Support for variable prediction horizons and context lengths. - Probabilistic Predictions: Generate both point forecasts and uncertainty estimates using a Student-T mixture model. - Extensive Pretraining on Large-Scale Data: Trained on over 2 trillion time series data points, the largest pretraining dataset for any open-weights time series foundation model to date. - Tailored for Observability Metrics with State-of-the-Art Performance on [GIFT-Eval](https://huggingface.co/spaces/Salesforce/GIFT-Eval) and [BOOM](https://huggingface.co/datasets/Datadog/BOOM). Overview of Toto-Open-Base-1.0 architecture. ### Training Data Summary [Read the full model card (483 words)](https://savrn.com/models/toto-open-base-1-0/card) ## Identity and Version Repository Datadog/Toto-Open-Base-1.0 Publisher Datadog Task Time series forecasting Modality Time series Library transformers Parameters 151M parameters Languages Not stated by the source Revision 0411ceb27bdf7fc3e4892e99edc8ad08192dc3c5 First published 2025-04-30 Last updated 2026-05-14 ## Files and Weights 6 files, 605.7 MB in total. The weights are 1 file totalling 605.2 MB in safetensors. Weights1 file · 605.2 MB Configuration1 file · 582 B Documentation1 file · 7.4 KB Other2 files · 450.3 KB Repository1 file · 1.6 KB Every file | File | Type | Size | SHA-256 | | --- | --- | --- | --- | | model.safetensors | Weights | 605.2 MB | 69b67e60decc | | config.json | Configuration | 582 B | — | | README.md | Documentation | 7.4 KB | — | | figures/architecture.png | Other | 238.8 KB | 74859ae9dbc0 | | figures/rankings.png | Other | 211.5 KB | aca8baf1aa78 | | .gitattributes | Repository | 1.6 KB | — | ## License and Download License apache-2.0 Access Open weights, no gate Download size 605.2 MB [Download from Datadog](https://huggingface.co/Datadog/Toto-Open-Base-1.0) Released by Datadog through its official repository on Hugging Face. [Read the license](https://www.apache.org/licenses/LICENSE-2.0). ## Built From - Described by arXiv:2505.14766 - Trained on (disclosed) [Salesforce/GiftEvalPretrain](https://savrn.com/datasets/giftevalpretrain) - Trained on (disclosed) autogluon/chronos_datasets ## Evaluations Each result is shown as reported, with the conditions its reporter stated. None is a SAVRN measurement. A comparison lines two results up only when their configuration, unit and setup are all stated and identical. | Benchmark | Conditions | Result | Reported by | Revision | Date | | --- | --- | --- | --- | --- | --- | | BOOM | Task time-series-forecastingMetric CRPSComparison conditions not established | 0.375 | [Datadog](https://huggingface.co/Datadog/Toto-Open-Base-1.0) Publisher reported | Evaluated revision not stated | — | | BOOM | Task time-series-forecastingMetric MASEComparison conditions not established | 0.617 | [Datadog](https://huggingface.co/Datadog/Toto-Open-Base-1.0) Publisher reported | Evaluated revision not stated | — | | GIFT-Eval | Task time-series-forecastingMetric CRPSComparison conditions not established | 0.517 | [Datadog](https://huggingface.co/Datadog/Toto-Open-Base-1.0) Publisher reported | Evaluated revision not stated | — | | GIFT-Eval | Task time-series-forecastingMetric MASEComparison conditions not established | 0.75 | [Datadog](https://huggingface.co/Datadog/Toto-Open-Base-1.0) Publisher reported | Evaluated revision not stated | — | ## Memory Requirements | Precision | Weights in memory | | --- | --- | | As published | 605.2 MB | | 16-bit | 0.3 GB | | 8-bit | 0.2 GB | | 4-bit | 0.1 GB | Weights only, from the published parameter count; the key-value cache and runtime add to this. ## Questions About Toto-Open-Base-1.0 ### How much GPU memory does Toto-Open-Base-1.0 need? About 0.4 GB at 16-bit and 0.1 GB at 4-bit: the weights (151M parameters) plus a working margin. A long context needs more. ### What is the cheapest GPU to run Toto-Open-Base-1.0 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 Toto-Open-Base-1.0 commercially? Yes. Toto-Open-Base-1.0 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 ### [sundial-base-128m](https://savrn.com/models/sundial-base-128m) [THUML @ Tsinghua University](https://savrn.com/model-publishers/thuml) News (2025.08) Sundial has been integrated into Apache IoTDB, a native time-series database. 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Recommended Python Version: Python 3.11 (support… Open weights mit 113M parameters transformers [View model](https://savrn.com/models/moment-1-base) ## Datadog [All models and datasets](https://savrn.com/model-publishers/datadog) ## Versions - [0411ceb27bdf](https://savrn.com/models/toto-open-base-1-0/versions/0411ceb27bdf) · current 2026-09-18 ## 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 2026-10-20. - [Hugging Face record](https://huggingface.co/Datadog/Toto-Open-Base-1.0) - [How the hub is built](https://savrn.com/model-hub/methodology)