# Kronos-Tokenizer-base by ShiYu: Open-Weight Model Source: https://savrn.com/models/kronos-tokenizer-base Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## Runs On What it takes to serve Kronos-Tokenizer-base (4M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them. | Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits | | --- | --- | --- | --- | --- | --- | | 16-bit | 0.0 GB | 0.0 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | | 8-bit | 0.0 GB | 0.0 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | | 4-bit | 0.0 GB | 0.0 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the [SAVRN Index](https://savrn.com/ai-index/pricing/gpus), read Oct 25, 2026. [Kronos-Tokenizer-base on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/kronos-tokenizer-base/gpus) ## SAVRN's Notes on Kronos-Tokenizer-base The memory line reads 0.0 GB at 16-bit, 8-bit and 4-bit, and that is no error. This is the tokenizer stage of Kronos, a decoder-only family built for financial candlesticks: it quantizes OHLCV K-line data into hierarchical discrete tokens for the autoregressive Transformer in stage two. At 4M parameters and 15.85 MB it will not decide your hardware. The cheapest option we list, a single MI300X with 192 GB at $1.85 an hour on-demand, is sized for the second stage, not this piece. MIT is as light as licenses get: commercial use, modification and redistribution are permitted as long as the copyright and permission notices travel with the files. The check is scope. This page carries no context length and no configuration block, so get the second-stage model and its limits before planning a deployment. Training data came from over 45 global exchanges; confirm yours are covered. SAVRN Research, 2026-09-18 ## Model Card By ShiYu, published under mit, revision 0e0117387f39. ### Kronos: A Foundation Model for the Language of Financial Markets Kronos is the first open-source foundation model for financial candlesticks (K-lines), trained on data from over 45 global exchanges. It is designed to handle the unique, high-noise characteristics of financial data. ### Introduction Kronos is a family of decoder-only foundation models, pre-trained specifically for the "language" of financial markets—K-line sequences. It leverages a novel two-stage framework: 1. A specialized tokenizer first quantizes continuous, multi-dimensional K-line data (OHLCV) into hierarchical discrete tokens. 2. A large, autoregressive Transformer is then pre-trained on these tokens, enabling it to serve as a unified model for diverse quantitative tasks. [Read the full model card (850 words)](https://savrn.com/models/kronos-tokenizer-base/card) ## Identity and Version Repository NeoQuasar/Kronos-Tokenizer-base Publisher ShiYu Task Time series forecasting Modality Time series Library pytorch Parameters 4M parameters Languages Not stated by the source Revision 0e0117387f39004a9016484a186a908917e22426 First published 2025-06-30 Last updated 2025-09-09 ## Files and Weights 4 files, 15.9 MB in total. The weights are 1 file totalling 15.8 MB in safetensors. Weights1 file · 15.8 MB Configuration1 file · 301 B Documentation1 file · 8.9 KB Repository1 file · 1.5 KB Every file | File | Type | Size | SHA-256 | | --- | --- | --- | --- | | model.safetensors | Weights | 15.8 MB | 59d85f6af76a | | config.json | Configuration | 301 B | — | | README.md | Documentation | 8.9 KB | — | | .gitattributes | Repository | 1.5 KB | — | ## License and Download License mit Access Open weights, no gate Download size 15.8 MB [Download from ShiYu](https://huggingface.co/NeoQuasar/Kronos-Tokenizer-base) Released by ShiYu through its official repository on Hugging Face. [Read the license](https://opensource.org/license/mit). ## Built From - Described by [arXiv:2508.02739](https://savrn.com/papers/kronos-a-foundation-model-for-the-language-of-financial-markets) ## Memory Requirements | Precision | Weights in memory | | --- | --- | | As published | 15.8 MB | | 16-bit | 0.0 GB | | 8-bit | 0.0 GB | | 4-bit | 0.0 GB | Weights only, from the published parameter count; the key-value cache and runtime add to this. ## Compare Kronos-Tokenizer-base - [Kronos-Tokenizer-base vs Toto-2.0-4m](https://savrn.com/models/compare/kronos-tokenizer-base-vs-toto-2-0-4m) - [Kronos-mini vs Kronos-Tokenizer-base](https://savrn.com/models/compare/kronos-mini-vs-kronos-tokenizer-base) - [Kronos-Tokenizer-2k vs Kronos-Tokenizer-base](https://savrn.com/models/compare/kronos-tokenizer-2k-vs-kronos-tokenizer-base) ## Questions About Kronos-Tokenizer-base ### How much GPU memory does Kronos-Tokenizer-base need? About 0 GB at 16-bit and 0 GB at 4-bit: the weights (4M parameters) plus a working margin. A long context needs more. ### What is the cheapest GPU to run Kronos-Tokenizer-base on? At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists. ### Can I use Kronos-Tokenizer-base commercially? Yes. Kronos-Tokenizer-base is released under MIT License. The MIT License is a short permissive license. It permits commercial use, modification and redistribution, provided the copyright notice and permission notice are included. ## Similar Models Model · Time series forecasting ### [Kronos-Tokenizer-2k](https://savrn.com/models/kronos-tokenizer-2k) [ShiYu](https://savrn.com/model-publishers/neoquasar) Kronos is the first open-source foundation model for financial candlesticks (K-lines), trained on data from over 45 global exchanges. It is designed to handle the unique, high-noise characteristics of financial data. Kronos is a family of decoder-only foundation models, pre-trained specifically for the "language" of financial markets—K-line sequences. It leverages a novel two-stage framework: 1. A specialized tokenizer first quantizes continuous, multi-dimensional K-line data (OHLCV) into hierarchical discrete tokens. 2. A large, autoregressive Transformer is then pre-trained on these tokens, enabling it to serve as a unified model for diverse quantitative tasks. The success of large-scale… Open weights mit 4M parameters torch [View model](https://savrn.com/models/kronos-tokenizer-2k) Model · Time series forecasting ### [Kronos-mini](https://savrn.com/models/kronos-mini) [ShiYu](https://savrn.com/model-publishers/neoquasar) Kronos is the first open-source foundation model for financial candlesticks (K-lines), trained on data from over 45 global exchanges. It is designed to handle the unique, high-noise characteristics of financial data. 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Refer to our paper for… Open weights apache-2.0 805,280 parameters granite-tsfm [View model](https://savrn.com/models/granite-timeseries-ttm-r1) ## ShiYu [All models and datasets](https://savrn.com/model-publishers/neoquasar) ## Versions - [0e0117387f39](https://savrn.com/models/kronos-tokenizer-base/versions/0e0117387f39) · current 2026-10-25 ## Explore More - [All time series forecasting models](https://savrn.com/models/tasks/time-series-forecasting) - [All models under mit](https://savrn.com/models/licenses/mit) - [Model comparisons](https://savrn.com/models/comparisons) - [The model directory](https://savrn.com/models) - [Open model prices by host](https://savrn.com/ai-index/pricing/open-models) ## Source - Repository metadata, read 2026-10-19. - [Hugging Face record](https://huggingface.co/NeoQuasar/Kronos-Tokenizer-base) - [How the hub is built](https://savrn.com/model-hub/methodology)