# Mistral releases Timestral, time-series forecasting models from 5M to 209M parameters that run on a CPU

> OnPrem World · https://onpremworld.com/en/news/2026-10-mistral-timestral · CC BY 4.0

Published: 2026-10-15 · Models

Mistral released Timestral on October 15, its first family of time-series foundation models, in four sizes of 5M, 28M, 123M and 209M parameters under a modified MIT licence, with weights on Hugging Face. The models give probabilistic, multivariate forecasts and accept past-only and known-future covariates, including categorical ones such as a machine's state. Mistral says the 209M model ranks first among zero-shot models on GIFT-Eval, TIME and fev-bench. No GPU is needed: on an 8-thread server CPU, a one-day forecast from three weeks of 15-minute data takes 10 ms with the 5M model and 140 ms with the 209M model, according to Mistral.

## Why it matters locally

Forecasting sensor, sales or capacity data can now run on the servers a company already has, with no graphics card and no data leaving the building.

## For the market

Mistral is moving beyond chat into the numbers side of enterprise data, where models such as TimesFM, Chronos and Falcon already compete; Mistral says its 209M model beats TimesFM-3 and Falcon-2.0 with fewer parameters. Because the models run on CPUs and can be deployed on-prem or air-gapped, they likely appeal to factories and finance teams that cannot send operational data to a cloud.

## In plain words

Timestral looks at a history of numbers, such as a machine's temperature every 15 minutes, and predicts what comes next along with how sure it is. It is like a weather forecast for your own data, and it is small enough to run on an ordinary office server.

## For practitioners

Start with the 123M model, which Mistral says comes within 0.2% of the 209M score at three quarters of its latency, or the 28M model where speed matters more. Read the modified MIT licence terms on the model card before production use, and compare zero-shot results with your current ARIMA or PatchTST baseline on your own series before switching; fine-tuning is offered through Mistral Forge.

## Source

[Introducing Timestral (Mistral AI)](https://mistral.ai/news/timestral)

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