{
 "schema_version": 1,
 "generated": "2026-10-31",
 "id": "amazon-science-chronos-forecasting--pick-a-chronos-model-size-for-your-workload",
 "repo": "amazon-science/chronos-forecasting",
 "repo_url": "https://github.com/amazon-science/chronos-forecasting",
 "repo_slug": "amazon-science-chronos-forecasting",
 "title": "Choose the Chronos model that fits your budget",
 "page_title": "How to choose the Chronos model that fits your budget with Chronos Forecasting",
 "summary": "Compare the listed model IDs and pick between Chronos-2, Chronos-Bolt and the original Chronos family.",
 "who": "A team deciding what to run on a CPU box or a small GPU.",
 "problem": "Running the largest model everywhere wastes time and memory.",
 "outcome": "A model choice you can justify, with the parameter count written down.",
 "steps": [
  "Read the Available Models table, which lists IDs from amazon/chronos-bolt-tiny at 9M up to amazon/chronos-t5-large at 710M",
  "Note that the README describes Chronos-Bolt as up to 250 times faster and 20 times more memory efficient than the original Chronos models of the same size",
  "Install the package with `pip install chronos-forecasting` and try the smallest candidate first",
  "For production the README recommends deploying Chronos-2 to Amazon SageMaker through AutoGluon-Cloud or SageMaker JumpStart"
 ],
 "agent_prompt": "Using amazon-science/chronos-forecasting, compare the listed Chronos-2, Chronos-Bolt and Chronos-T5 model IDs for <my forecasting workload> and recommend one with the reasoning.",
 "agents": [
  "claude-code",
  "codex",
  "cursor",
  "gemini-cli"
 ],
 "difficulty": "Beginner",
 "time": "30 minutes",
 "basis": "readme",
 "caveat": "The benchmark claims in the README are the authors' own.",
 "category": "Data and analytics",
 "tags": [
  "Data and analytics"
 ],
 "lang": "",
 "requirements": [
  "Python with pip and `pip install chronos-forecasting`",
  "pandas with pyarrow for the DataFrame examples",
  "A CUDA GPU for the README example, which sets device_map to cuda"
 ],
 "risk": "none",
 "seen_in_the_wild": false,
 "mentioned_publicly": false,
 "risks": [
  {
   "tag": "cost",
   "notice": "The recommended production route is Amazon SageMaker, which is billed by AWS."
  }
 ],
 "best": false,
 "score": 0.8,
 "evidence": [],
 "url": "https://dedupedai.com/use-cases/amazon-science-chronos-forecasting/pick-a-chronos-model-size-for-your-workload/",
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 "disclaimer": "Deduped lists open-source repositories that belong to other people. We do not own, maintain, audit, or vouch for any of them, and a repo appearing here is not an endorsement or a safety check. The use cases, steps, and prompts are written by AI and may be incomplete, outdated, or just wrong. Where a use case is marked \"Seen in the wild\", we link to public posts where someone described something similar, but we have not verified those posts and the use case itself is still our AI's own write-up. Use all of this at your own risk. Only run code, commands, or agents on systems and accounts you are authorized to use, and read the code and every command before you run it, especially anything that touches credentials, files, or the network. Nothing here is professional, security, legal, or financial advice. All trademarks and project names belong to their respective owners, and their use here does not imply affiliation. Content is provided as is, without warranties of any kind. Found a mistake, a safety problem, or something you want removed? Email hello@dedupedai.com and we will look at it promptly.",
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}