TimesFM 2.5 200M
onlinegoogle/timesfm-2.5-200m-pytorch200M (+ optional 30M quantile head) params | 512 context | $0.5000 input | $1.50 output
TimesFM 2.5 200M is Google's smaller and more deployment-friendly open TimesFM checkpoint. The official repo describes it as a 16K-context model with an optional continuous quantile head, no frequency indicator requirement, and speed-oriented structural updates such as QKV fusion. It is a strong choice when you want modern TimesFM behavior with much lower model size than the 500M release.
Model Classification
Family
TimesFM
Type
time series foundation model
Pretrained time-series model exposed on TSFM.ai for zero-shot or few-shot forecasting workloads.
Resources
Training Data
GiftEvalPretrain, Wikimedia Pageviews through November 2023, Google Trends through end-2022, plus synthetic and augmented data, as listed in the official model card.
Recommended For
- • Long-context zero-shot forecasting with strong open-model baselines
- • Workloads where point forecasting quality matters more than broad task coverage
Strengths
- • Large open checkpoints with long context windows
- • Efficient patched-transformer design with strong zero-shot behavior
Limitations
- • Primarily a forecasting family rather than a general multi-task TSFM
- • Quantile support is not the main identity of the family
Capabilities
Tags
Specifications
- Parameters
- 200M (+ optional 30M quantile head)
- Architecture
- decoder-only patched transformer with optional continuous quantile head
- Context length
- 512
- Max output
- 1,024
- Avg latency
- n/a
- Uptime
- n/a
- Rate limit
- n/a
- Accelerator
- NVIDIA GPU
- Regions
- Virginia, US
- License
- n/a
Pricing
- Input / 1M tokens
- $0.5000
- Output / 1M tokens
- $1.50
Performance
- Average latency
- n/a
- Availability
- n/a
- Rate limit
- n/a