The AI Museum
AI Analytics1,072 recordsRecounted 23 September 2026

Models in 2018

Weightsopenclosedunknownfrontier only
Year20262025202420232022202120202019201820172016
1 filter active, showing 37 of 1,072 recordsClear all

37

Models

22 organisations

30%

Open weights

9 open, 21 closed

1

Frontier

By Epoch's flag

5B

Largest traceable

Mesh-TensorFlow Transformer 4.9B (language)

2018

Span

15 with a traceable size

Releases

by month
Jan
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Organisation

Country

Domain

Records

37
Models in 2018, newest first. Each row links to the source Epoch AI records for it.
ModelOrganisationReleasedParametersWeights
SPN (ImageNet 128)Google Brain2018-12250MClosed
StyleGANNVIDIA2018-1226MOpen
Fine-tuned-AWD-LSTM-DOC (fin)Samsung R&D Institute Russia2018-1146MClosed
GPipe (Transformer)Google2018-116B *Not recorded
Mesh-TensorFlow Transformer 2.9B (translation)Google Brain2018-113B *Closed
Mesh-TensorFlow Transformer 4.9B (language)Google Brain2018-115BClosed
Multi-cell LSTMUniversity of Hyderabad2018-117M *Closed
BERT-LargeGoogle2018-10340MOpen
MemoReaderSamsung2018-10—Closed
MetaMimicGoogle2018-1022M *Not recorded
TrellisNetCarnegie Mellon University (CMU)2018-10180MClosed
AWD-LSTM-MoS + dynamic evaluation (WT2, 2018)Peking University2018-0935MClosed
LSTM+NeuralCacheKU Leuven2018-092M *Closed
NetSurfP-2.0Technical University of Denmark2018-09—Closed
Transformer (Adaptive Input Embeddings) WT103Facebook AI Research2018-09247MOpen
Transformer + Simple Recurrent UnitASAPP2018-0990MClosed
(ensemble): AWD-LSTM-DOC (fin) × 5 (WT2)NTT Communication Science Laboratories2018-08185MOpen
AWD-LSTM-MoS+PDR + dynamic evaluation (WT2)IBM2018-0835MClosed
Big Transformer for Back-TranslationFacebook AI Research2018-08—Open
Big-Little NetIBM2018-0777M *Open
Big-Little Net (speech)IBM2018-073M *Open
FTW (For The Win)DeepMind2018-07126M *Closed
GPT-1OpenAI2018-06117M *Open
MobileNetV2Google2018-063M *Not recorded
Relational Memory CoreDeepMind2018-06—Closed
aLSTM(depth-2)+RecurrentPolicy (WT2)University of Manchester2018-0532MClosed
Dropout-LSTM+Noise(Bernoulli) (WT2)Columbia University2018-0551M *Closed
ResNeXt-101 32x48dFacebook2018-05829MOpen
YOLOv3University of Washington2018-0457M *Closed
4 layer QRNN (h=2500)Salesforce Research2018-03151M *Closed
Chinese - English translationMicrosoft2018-03—Not recorded
DeepLabV3+Google2018-02—Not recorded
ELMoUniversity of Washington2018-0294M *Not recorded
IMPALADeepMind2018-022MClosed
QRNNSalesforce Research2018-02135M *Closed
TCN (P-MNIST)Carnegie Mellon University (CMU)2018-0242,000Not recorded
T-DMCAGoogle Brain2018-01—Closed

An asterisk marks a parameter figure the source does not consider traceable to the people who built the model. That judgement is the source’s and it has false negatives: Kimi K3 is marked speculative at 2.8T while Moonshot’s own model card states the figure. Why that distinction carries the whole exhibit.

Counts are of models Epoch AI records as notable, not of every model released. The most recent month is always incomplete, because a model enters the dataset when it is reviewed rather than when it ships.

Epoch AI, 'Data on AI Models'. Published online at epoch.ai. Retrieved from 'https://epoch.ai/data/ai-models-documentation' Used under CC BY 4.0.

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