Toto (Time Series Optimized Transformer for Observability) is a family of time series foundation models for multivariate forecasting developed by Datadog. Toto 2.0 is the current generation, featuring u-μP-scaled transformers ranging from 4m to 2.5B parameters, all trained from a single recipe. Forecast quality improves reliably with parameter count across the family.

The family sets a new state of the art on three forecasting benchmarks: BOOM, our observability benchmark; GIFT-Eval, the standard general-purpose benchmark; and the recent contamination-resistant TIME benchmark.

📊 Performance

⚡ Quick Start

Inference code is available on GitHub.

Installation

pip install toto-models

Inference Example

python

import torch
from toto2 import Toto2Model

model = Toto2Model.from_pretrained("Datadog/Toto-2.0-22m")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device).eval()

# (batch, n_variates, time_steps)
target = torch.randn(1, 1, 512, device=device)
target_mask = torch.ones_like(target, dtype=torch.bool)
series_ids = torch.zeros(1, 1, dtype=torch.long, device=device)

# Returns quantiles of shape (9, batch, n_variates, horizon)
# Quantile levels: [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]
quantiles = model.forecast(
    {"target": target, "target_mask": target_mask, "series_ids": series_ids},
    horizon=96,
    decode_block_size=768,
    has_missing_values=False,
)

For more examples, see the Quick Start notebook and GluonTS integration notebook.

💾 Available Checkpoints

All five Toto 2.0 sizes share the same training recipe; pick a size based on your accuracy/latency budget. Latency is forward-pass time for a 1,024-step single-pass forecast at batch size 8 on a single A100.

Model	Params	Weights (fp32)	Latency	Recommended for
Toto‑2.0‑4m	4m	16 MB	~3.8 ms	Edge / CPU deployment; tightest latency or memory budgets.
Toto‑2.0‑22m	22m	84 MB	~5.0 ms	Efficient default — matches or beats Toto 1.0 quality with ~7× fewer parameters.
Toto‑2.0‑313m	313m	1.2 GB	~15.4 ms	Strong general-purpose checkpoint; top-3 foundation model on GIFT-Eval.
Toto‑2.0‑1B	1B	3.9 GB	~20.9 ms	Best quality / cost tradeoff for production workloads.
Toto‑2.0‑2.5B	2.5B	9.1 GB	~36.2 ms	Highest accuracy; #1 foundation model on every benchmark.

✨ Key Features

Zero-Shot Forecasting: Forecast without fine-tuning on your specific time series.
Multi-Variate Support: Efficiently process multiple variables using alternating time/variate attention.
Probabilistic Predictions: Generate point forecasts and uncertainty estimates via a quantile output head.
Decoder-Only Architecture: Support for variable prediction horizons and context lengths.
u-μP Scaling: A single training recipe transfers cleanly across all five sizes (4m → 2.5B).

🏗️ Architecture

🔗 Additional Resources

Technical Report
Blog Post
GitHub Repository
Toto 2.0 Collection — all five base checkpoints
BOOM Dataset — Datadog's observability time-series benchmark
Toto 1.0 Weights

📖 Citation

bibtex

@misc{khwaja2026toto20timeseries,
      title={Toto 2.0: Time Series Forecasting Enters the Scaling Era}, 
      author={Emaad Khwaja and Chris Lettieri and Gerald Woo and Eden Belouadah and Marc Cenac and Guillaume Jarry and Enguerrand Paquin and Xunyi Zhao and Viktoriya Zhukov and Othmane Abou-Amal and Chenghao Liu and Ameet Talwalkar and David Asker},
      year={2026},
      eprint={2605.20119},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2605.20119}, 
}