sundial-base-128m
Sundial is a 128M-parameter generative time-series foundation model pre-trained on 1 trillion time points. It performs zero-shot point and probabilistic forecasting by generating multiple plausible future sequences. The model uses a decoder-only Transformer with a TimeFlow loss function to produce non-deterministic predictions, enabling statistical analysis (quantiles, confidence intervals) from sample sets rather than single-point estimates.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | thuml |
| Parameters | 128M |
| Context window | Unknown |
| License | apache-2.0 — OSI-approved |
| Modality / task | time-series-forecasting |
| Gated on HuggingFace | No |
| Downloads | 148.4k |
| Likes | 78 |
| Last updated | 2026-03-09 |
| Source | thuml/sundial-base-128m |
What sundial-base-128m is
Sundial is a causal Transformer (decoder-only, 12 layers, FP32) designed for univariate time-series forecasting. It patches input sequences (patch length 16), embeds them, and applies auto-regressive decoding with TimeFlow loss—a flow-matching-based objective that models per-token probability distributions. Supports lookback up to 2880 steps, multi-patch prediction up to 720 steps, and generates multiple samples for probabilistic forecasting. Includes optimizations (KV Cache, FlashAttention, FlashAttention-compatible). Pre-trained on 1032B time points from public datasets (UTSD, Salesforce/lotsa_data, autogluon/chronos_datasets).
Run sundial-base-128m locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="thuml/sundial-base-128m")out = pipe("Explain retrieval-augmented generation in one sentence.", max_new_tokens=128)print(out[0]["generated_text"])Swap in vLLM or Ollama for production-grade serving. DEV.co can stand up the inference stack.
How you'd run it
A typical self-hosted path — open weights, an inference server, your application.
DEV.co builds each layer — from GPU infrastructure to the application.
Best use cases
Running & fine-tuning it
128M parameters, FP32 precision. VRAM estimate: ~512 MB for model weights + ~1–2 GB working memory (batch inference, KV cache). CPU inference feasible (M1 Pro ~500ms); A100 GPU provides 4–8× speedup. Transformers 4.40.1 + Python 3.10 required for stability.
Card does not document fine-tuning procedures, LoRA, or QLoRA feasibility. Model includes custom_code tag; trust_remote_code=True required for loading. Fine-tuning approach unknown—requires review of GitHub repo (thuml/Sundial) or paper (arxiv:2502.00816) for guidance. Decoder-only architecture theoretically supports parameter-efficient tuning, but no official instructions provided.
When to avoid it — and what to weigh
- Multivariate or High-Dimensional Time-Series — Card does not explicitly state multivariate capability. Use case appears limited to univariate sequences; review paper (arxiv:2502.00816) or notebook for multi-channel support.
- Very Long Context Windows — Maximum context length is 2880 steps. If your forecasting task requires lookback windows larger than this, architecture is not suitable without re-training or aggregation strategies.
- Sub-millisecond Latency Requirements — Inference on M1 Pro is 500ms+ for typical workloads. GPU (A100) is faster but still not ultra-low-latency; unsuitable for real-time control or high-frequency trading.
- Deterministic Single-Point Predictions Only — Model is designed for generative sampling. If you need only a single deterministic forecast, added complexity of sampling and aggregation may be unnecessary overhead.
License & commercial use
Apache-2.0 license. Permissive OSI-approved license allowing modification, distribution, and commercial use, provided original copyright and license notice are retained.
Apache-2.0 is a permissive OSI license that explicitly permits commercial use. No gating or restrictions stated. However, review your use case against Apache-2.0 terms (warranty disclaimers, liability limits). No commercial support SLA or indemnification mentioned; consult Legal if production deployment requires SLA.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | Moderate |
| DEV.co fit | Strong |
| Assessment confidence | High |
Model uses trust_remote_code=True for custom_code loading; review source before deployment in restricted environments. Pre-trained on public internet data (UTSD, Salesforce, autogluon datasets)—inherited data quality/bias risks unknown. No explicit threat model, adversarial robustness, or data provenance audit stated. Consider input validation and output monitoring for production time-series systems.
Alternatives to consider
Chronos (Amazon/Salesforce)
Similar 128M architecture for zero-shot time-series forecasting. Cited in card (arxiv:2403.07815). Compare training scale, inference speed, and multivariate support for your use case.
Time Series Transformer (Hugging Face / Google)
Established baseline for univariate forecasting. Less recent but mature documentation and broader framework support (JAX, TensorFlow, PyTorch). No probabilistic generation.
NeuralProphet or StatsForecast
Lighter-weight, interpretable alternatives for time-series forecasting. Lower inference cost; no generative sampling but faster deployment in resource-constrained environments.
Ship sundial-base-128m with senior software developers
Sundial is open-source (Apache-2.0) and ready for production. Start with the quickstart code, benchmark against your data, and evaluate on GIFT-Eval or Time-Series-Library. Contact the authors or consult the GitHub repo for fine-tuning guidance.
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sundial-base-128m FAQ
Can I use Sundial for commercial applications?
What hardware do I need to run Sundial?
Does Sundial support multivariate time-series?
How do I fine-tune Sundial on my own data?
Software developers & web developers for hire
Need help beyond evaluating sundial-base-128m? DEV.co is a software development agency offering software development services and web development for teams of every size. Our software developers and web developers build custom software, web applications, APIs, and open-source llms integrations — and maintain them long-term.
Ready to Deploy Time-Series Forecasting?
Sundial is open-source (Apache-2.0) and ready for production. Start with the quickstart code, benchmark against your data, and evaluate on GIFT-Eval or Time-Series-Library. Contact the authors or consult the GitHub repo for fine-tuning guidance.