# t5-small by T5 community: Open-Weight Model Source: https://savrn.com/models/t5-small Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## Runs On What it takes to serve t5-small (61M 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 | 0.1 GB | 0.1 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 | 0.1 GB | 0.1 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 23, 2026. [t5-small on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/t5-small/gpus) ## SAVRN's Notes on t5-small The 4-bit row reads 0.0 GB and the 16-bit row 0.1 GB, so quantizing buys nothing worth the trouble. The cheapest setup we list is a 192 GB MI300X at $1.85 an hour, which makes the only hardware question what else that card does. What you get is the 61M-parameter checkpoint of the text-to-text transfer transformer, pretrained on C4 with a mix of unsupervised and supervised tasks, labeled for translation. Apache 2.0 means you can fine-tune it, ship the result and sell it, keeping the notices and stating what you changed. No context length is listed, so measure your longest input against the model yourself. The seven papers listed for it are task datasets, acceptability judgments, semantic textual similarity, inference, SQuAD, word in context, reading comprehension and yes-or-no questions, the trail for what the supervised side covered. The last update was 2023-06-30. SAVRN Research, 2026-09-18 ## Model Card By T5 community, published under apache-2.0, revision df1b051c4962. ### Model Card for T5 Small ### Table of Contents 1. Model Details 2. Uses 3. Bias, Risks, and Limitations 4. Training Details 5. Evaluation 6. Environmental Impact 7. Citation 8. Model Card Authors 9. How To Get Started With the Model ### Model Details ### Model Description The developers of the Text-To-Text Transfer Transformer (T5) [write](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html): With T5, we propose reframing all NLP tasks into a unified text-to-text-format where the input and output are always text strings, in contrast to BERT-style models that can only output either a class label or a span of the input. Our text-to-text framework allows us to use the same model, loss function, and hyperparameters on any NLP task. T5-Small is the checkpoint with 60 million parameters. [Read the full model card (802 words)](https://savrn.com/models/t5-small/card) ## Configuration Architecture T5ForConditionalGeneration Vocabulary size 32,128 Model type t5 ## Identity and Version Repository google-t5/t5-small Publisher T5 community Task Translation Modality Text Library transformers Parameters 61M parameters Languages en, fr, ro, de Revision df1b051c49625cf57a3d0d8d3863ed4d13564fe4 First published 2022-03-02 Last updated 2023-06-30 ## Files and Weights 20 files, 2.2 GB in total. The weights are 13 files totalling 2.2 GB in bin, h5, msgpack, onnx, ot, safetensors. Weights13 files · 2.2 GB Configuration2 files · 1.4 KB Tokenizer3 files · 2.2 MB Documentation1 file · 8.5 KB Repository1 file · 537 B Every file | File | Type | Size | SHA-256 | | --- | --- | --- | --- | | flax_model.msgpack | Weights | 242.0 MB | b143e13ccb73 | | model.safetensors | Weights | 242.0 MB | bd944e5f1b3a | | onnx/decoder_model.onnx | Weights | 232.5 MB | 2256c6365e82 | | onnx/decoder_model_merged.onnx | Weights | 232.6 MB | f6d2874dafd2 | | onnx/decoder_model_merged_quantized.onnx | Weights | 58.7 MB | af6a06ea8488 | | onnx/decoder_model_quantized.onnx | Weights | 58.4 MB | a1b7a03fd50c | | onnx/decoder_with_past_model.onnx | Weights | 219.9 MB | 2eb7790f56c4 | | onnx/decoder_with_past_model_quantized.onnx | Weights | 55.2 MB | 30e4b22fc986 | | onnx/encoder_model.onnx | Weights | 141.4 MB | 3ebb633a3a81 | | onnx/encoder_model_quantized.onnx | Weights | 35.5 MB | 5124770e67bf | | pytorch_model.bin | Weights | 242.1 MB | dd8c1c79a54c | | rust_model.ot | Weights | 242.1 MB | 636b17628823 | | tf_model.h5 | Weights | 242.3 MB | fc9d2ec530ef | | config.json | Configuration | 1.2 KB | — | | generation_config.json | Configuration | 147 B | — | | README.md | Documentation | 8.5 KB | — | | .gitattributes | Repository | 537 B | — | | spiece.model | Tokenizer | 791.7 KB | — | | tokenizer.json | Tokenizer | 1.4 MB | — | | tokenizer_config.json | Tokenizer | 2.3 KB | — | ## License and Download License apache-2.0 Access Open weights, no gate Download size 2.2 GB [Download from T5 community](https://huggingface.co/google-t5/t5-small) Released by T5 community through its official repository on Hugging Face. [Read the license](https://www.apache.org/licenses/LICENSE-2.0). ## Built From - Described by [arXiv:1606.05250](https://savrn.com/papers/squad-100-000-questions-for-machine-comprehension-of-text) - Described by [arXiv:1704.05426](https://savrn.com/papers/a-broad-coverage-challenge-corpus-for-sentence-understanding-through-inference) - Described by [arXiv:1708.00055](https://savrn.com/papers/semeval-2017-task-1-semantic-textual-similarity-multilingual-and-cross-lingual-focused-eva) - Described by [arXiv:1805.12471](https://savrn.com/papers/neural-network-acceptability-judgments) - Described by [arXiv:1808.09121](https://savrn.com/papers/wic-the-word-in-context-dataset-for-evaluating-context-sensitive-meaning-representations) - Described by [arXiv:1810.12885](https://savrn.com/papers/record-bridging-the-gap-between-human-and-machine-commonsense-reading-comprehension) - Described by [arXiv:1905.10044](https://savrn.com/papers/boolq-exploring-the-surprising-difficulty-of-natural-yes-no-questions) - Described by [arXiv:1910.09700](https://savrn.com/papers/quantifying-the-carbon-emissions-of-machine-learning) - Trained on (disclosed) c4 ## Memory Requirements | Precision | Weights in memory | | --- | --- | | As published | 2.2 GB | | 16-bit | 0.1 GB | | 8-bit | 0.1 GB | | 4-bit | 0.0 GB | Weights only, from the published parameter count; the key-value cache and runtime add to this. ## Built on This Model - Derived from[basic-chat-model](https://savrn.com/models/basic-chat-model) ## Compare t5-small - [opus-mt-fr-en vs t5-small](https://savrn.com/models/compare/opus-mt-fr-en-vs-t5-small) - [opus-mt-tc-big-ko-en vs t5-small](https://savrn.com/models/compare/opus-mt-tc-big-ko-en-vs-t5-small) - [t5-base vs t5-small](https://savrn.com/models/compare/t5-base-vs-t5-small) ## Questions About t5-small ### How much GPU memory does t5-small need? About 0.1 GB at 16-bit and 0 GB at 4-bit: the weights (61M parameters) plus a working margin. A long context needs more. ### What is the cheapest GPU to run t5-small 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 t5-small commercially? Yes. t5-small is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors. ## Similar Models Model · Translation ### [opus-mt-fr-en](https://savrn.com/models/opus-mt-fr-en) [Helsinki-NLP Research Group](https://savrn.com/model-publishers/helsinki-nlp) source languages: fr; target languages: en; OPUS readme: fr-en; dataset: opus; model: transformer-align; pre-processing: normalization + SentencePiece. Open weights apache-2.0 75M parameters 512 tokens transformers [View model](https://savrn.com/models/opus-mt-fr-en) Model · Translation ### [transformer-en-zh](https://savrn.com/models/transformer-en-zh) [Chou Lucas](https://savrn.com/model-publishers/chou-lucas) 基于标准 Transformer（Post-LN + 固定 sin-cos 位置编码）的英译中模型， 由本工程用 HuggingFace Seq2SeqTrainer 训练得到（checkpoint：trained with Seq2SeqTrainer (standard HF)）。 本仓库是标准 HF 仓库：包含 config.json + automap，代码随仓库分发， 因此任何人在 HF 上 trustremotecode=True 即可直接加载使用，无需本工程的其它文件。 逐步自回归推理，未实现 KV Cache（usecache=false），长句偏慢。 生成质量以 beam search（numbeams=3）优于贪心。 请按自身场景补充 LICENSE。 Open weights 98M parameters 5,000 tokens transformers [View model](https://savrn.com/models/transformer-en-zh) Model · Translation ### [opus-mt-nl-en](https://savrn.com/models/opus-mt-nl-en) [Helsinki-NLP Research Group](https://savrn.com/model-publishers/helsinki-nlp) source languages: nl; target languages: en; OPUS readme: nl-en; dataset: opus; model: transformer-align; pre-processing: normalization + SentencePiece. Open weights apache-2.0 512 tokens transformers [View model](https://savrn.com/models/opus-mt-nl-en) Model · Translation ### [nllb-200-distilled-600M](https://savrn.com/models/nllb-200-distilled-600m) [AI at Meta](https://savrn.com/model-publishers/facebook) This is the model card of NLLB-200's distilled 600M variant. Here are the metrics for that particular checkpoint. - Information about training algorithms, parameters, fairness constraints or other applied approaches, and features. The exact training algorithm, data and the strategies to handle data imbalances for high and low resource languages that were used to train NLLB-200 is described in the paper. - Paper or other resource for more information NLLB Team et al, No Language Left Behind: Scaling Human-Centered Machine Translation, Arxiv, 2022 - Where to send questions or comments about the model: https://github.com/facebookresearch/fairseq/issues • Model performance measures: NLLB-200… Open weights cc-by-nc-4.0 1,024 tokens transformers [View model](https://savrn.com/models/nllb-200-distilled-600m) Model · Translation ### [opus-mt-en-ru](https://savrn.com/models/opus-mt-en-ru) [Helsinki-NLP Research Group](https://savrn.com/model-publishers/helsinki-nlp) source languages: en; target languages: ru; OPUS readme: en-ru; dataset: opus; model: transformer-align; pre-processing: normalization + SentencePiece. Open weights apache-2.0 512 tokens transformers [View model](https://savrn.com/models/opus-mt-en-ru) Model · Translation ### [opus-mt-en-de](https://savrn.com/models/opus-mt-en-de) [Helsinki-NLP Research Group](https://savrn.com/model-publishers/helsinki-nlp) This model can be used for translation and text-to-text generation. CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propagate historical and current stereotypes. Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). Further details about the dataset for this model can be found in the OPUS readme: en-de Open weights cc-by-4.0 512 tokens transformers [View model](https://savrn.com/models/opus-mt-en-de) ## T5 community [All models and datasets](https://savrn.com/model-publishers/google-t5) ## Versions - [df1b051c4962](https://savrn.com/models/t5-small/versions/df1b051c4962) · current 2026-10-23 ## Explore More - [All translation models](https://savrn.com/models/tasks/translation) - [All models under apache-2.0](https://savrn.com/models/licenses/apache-2-0) - [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. - [T5 community website](https://github.com/google-research/text-to-text-transfer-transformer) - [Hugging Face record](https://huggingface.co/google-t5/t5-small) - [How the hub is built](https://savrn.com/model-hub/methodology)