Moirai-2.0-R-Small
onlineSalesforce/moirai-2.0-R-small~11M params | 512 context | $0.00025 per forecast
Moirai-2.0-R-Small is the faster successor to the first dense Moirai family. The official Moirai 2.0 release switches to a decoder-only design with quantile loss, multi-token prediction, and better missing-value handling, while Salesforce reports performance that surpasses larger earlier Moirai checkpoints. TSFM.ai serves the published 512-step configuration for this family rather than advertising longer unsupported histories.
Model Classification
Family
Moirai
Type
time series foundation model
Pretrained time-series model exposed on TSFM.ai for zero-shot or few-shot forecasting workloads.
Resources
Training Data
Subset of GIFT-Eval Pretrain and Train, Chronos mixup data, KernelSynth synthetic series, and internal Salesforce operational data, as listed in the official model card.
Recommended For
- • Multivariate forecasting across heterogeneous domains
- • Workloads that benefit from probabilistic outputs and arbitrary variate counts
Strengths
- • Strong multivariate coverage across the Moirai family
- • Well-suited to covariates and correlated series
Limitations
- • Model cards for some newer Moirai variants are still sparse on exact checkpoint details
- • Heavier family choices can be more expensive than tiny single-purpose baselines
Capabilities
Tags
Specifications
- Parameters
- ~11M
- Architecture
- decoder-only transformer with quantile loss and multi-token prediction
- Context length
- 512
- Max context
- 8,192
- Minimum history
- n/a
- Recommended history
- n/a
- Input step
- n/a
- Required target series
- 1
- Temperature
- Ignored
- Top P
- Ignored
- Max output
- 1,024
- Avg latency
- n/a
- Uptime
- n/a
- Plan limits
- 1,000 rpm free · 1,000,000 rpm with billing
- Accelerator
- T4
- Regions
- Virginia, US
- License
- n/a
Pricing
- Per forecast
- $0.00025
Performance
- Average latency
- n/a
- Availability
- n/a
- Plan limits
- 1,000 rpm free · 1,000,000 rpm with billing