{"id": 1015504, "name": "Number of parameters", "unit": "", "createdAt": "2025-03-15T08:53:17.000Z", "updatedAt": "2026-03-08T06:31:58.000Z", "coverage": "", "timespan": "", "datasetId": 6999, "shortUnit": "", "columnOrder": 0, "shortName": "parameters", "catalogPath": "grapher/artificial_intelligence/2025-03-12/epoch/epoch#parameters", "descriptionShort": "Total number of learnable variables or weights that the model contains. Parameters are adjusted during the training process to optimize the model's performance.", "type": "int", "dataChecksum": "4938713603184295351", "metadataChecksum": "3450693395518952827", "datasetName": "Parameter, Compute and Data Trends in Machine Learning", "updatePeriodDays": 31, "datasetVersion": "2025-03-12", "nonRedistributable": false, "display": {"zeroDay": "1949-01-01", "yearIsDay": true}, "schemaVersion": 2, "processingLevel": "major", "presentation": {"topicTagsLinks": ["Artificial Intelligence"]}, "descriptionKey": ["Parameters are internal variables that machine learning models adjust during their training process to improve their ability to make accurate predictions. They act as the model's \"knobs\" that are fine-tuned based on the provided data. In deep learning, a subset of artificial intelligence (AI), parameters primarily consist of the weights assigned to the connections between the small processing units called neurons. Picture a vast network of interconnected neurons where the strength of each connection represents a parameter.", "The total number of parameters in a model is influenced by various factors. The model's structure and the number of \u201clayers\u201d of neurons play a significant role. Generally, more complex models with additional layers tend to have a higher number of parameters. Special components of specific deep learning architectures can further contribute to the overall parameter count.", "Understanding the number of parameters in a model is crucial to design effective models. More parameters can help the model understand complex data patterns, potentially leading to higher accuracy. However, there's a fine balance to strike. If a model has too many parameters, it risks memorizing the specific examples in its training data rather than learning their underlying patterns. Consequently, it may perform poorly when presented with new, unseen data. Achieving the right balance of parameters is a critical consideration in model development.", "In recent times, the AI community has witnessed the emergence of what are often referred to as \"giant models.\" These models boast an astounding number of parameters, reaching into the billions or even trillions. While these huge models have achieved remarkable performance, they have a significant computational cost. Effectively managing and training such large-scale models has become a prominent and active area of research and discussion within the AI field."], "dimensions": {"years": {"values": [{"id": 25443}, {"id": 24432}, {"id": 24820}, {"id": 25282}, {"id": 26512}, {"id": 28122}, {"id": 23936}, {"id": 4198}, {"id": 24096}, {"id": 26428}, {"id": 27603}, {"id": 25835}, {"id": 25971}, {"id": 26459}, {"id": 27534}, {"id": 26994}, {"id": 16403}, {"id": 26986}, {"id": 12661}, {"id": 25035}, {"id": 25727}, {"id": 25055}, {"id": 25105}, {"id": 25700}, {"id": 25150}, {"id": 25462}, {"id": 25427}, {"id": 26684}, {"id": 23892}, {"id": 26476}, {"id": 25834}, {"id": 28017}, {"id": 23283}, {"id": 26876}, {"id": 26695}, {"id": 25946}, {"id": 26266}, {"id": 27409}, {"id": 24379}, {"id": 25127}, {"id": 27292}, {"id": 25869}, {"id": 25841}, {"id": 27298}, {"id": 27043}, {"id": 27740}, {"id": 27456}, {"id": 27091}, {"id": 27234}, {"id": 27435}, {"id": 25868}, {"id": 26620}, {"id": 27071}, {"id": 26896}, {"id": 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"code": null}, {"id": 368007, "name": "RNN+LDA+KN5+cache", "code": null}, {"id": 368752, "name": "RNN-WER", "code": null}, {"id": 369026, "name": "RT-1", "code": null}, {"id": 369038, "name": "RT-2", "code": null}, {"id": 369523, "name": "RT-2-X", "code": null}, {"id": 370522, "name": "RankNet", "code": null}, {"id": 306156, "name": "Rational DQN Average", "code": null}, {"id": 369542, "name": "ReALM", "code": null}, {"id": 306030, "name": "ReLU (NORB)", "code": null}, {"id": 369974, "name": "ReLU-Speech", "code": null}, {"id": 370170, "name": "Reka Core", "code": null}, {"id": 372332, "name": "ResNeXt-101 (64\u00d74d)", "code": null}, {"id": 306104, "name": "ResNeXt-101 32x48d", "code": null}, {"id": 306114, "name": "ResNeXt-101 Billion-scale", "code": null}, {"id": 306077, "name": "ResNeXt-50", "code": null}, {"id": 367493, "name": "ResNet-1001", "code": null}, {"id": 371899, "name": "ResNet-101 (ImageNet)", "code": null}, {"id": 306065, "name": "ResNet-110 (CIFAR-10)", "code": null}, {"id": 257028, "name": "ResNet-152 (ImageNet)", "code": null}, {"id": 308274, "name": "RetinaNet-R101", "code": null}, {"id": 306090, "name": "RetinaNet-R50", "code": null}, {"id": 368733, "name": "Retrieval-Augmented Generator", "code": null}, {"id": 365388, "name": "RoBERTa Large", "code": null}, {"id": 372317, "name": "RoFormer", "code": null}, {"id": 367633, "name": "Robot Parkour", "code": null}, {"id": 370529, "name": "Routing Transformer (WT-103)", "code": null}, {"id": 368092, "name": "S4", "code": null}, {"id": 371882, "name": "SAF R-CNN", "code": null}, {"id": 369976, "name": "SB-LM", "code": null}, {"id": 257089, "name": "SEER", "code": null}, {"id": 306093, "name": "SENet (ImageNet)", "code": null}, {"id": 305969, "name": "SNARC", "code": null}, {"id": 370721, "name": "SNM-skip", "code": null}, {"id": 369979, "name": "SOM-CNN", "code": null}, {"id": 369144, "name": "SPHINX (Llama 2 13B)", "code": null}, {"id": 369001, "name": "SPIDER2", "code": null}, {"id": 371826, "name": "SPN (ImageNet 128)", "code": null}, {"id": 368042, "name": "SPN-4+KN5", "code": null}, {"id": 368098, "name": "SRU++ Large", "code": null}, {"id": 368348, "name": "ST-MoE", "code": null}, {"id": 371974, "name": "STORM-B/8", "code": null}, {"id": 371873, "name": "SVM-CNN", "code": null}, {"id": 256994, "name": "Samuel Neural Checkers", "code": null}, {"id": 368005, "name": "Sandwich Transformer", "code": null}, {"id": 369176, "name": "SciBERT", "code": null}, {"id": 369169, "name": "SeamlessM4T", "code": null}, {"id": 372161, "name": "Seed-1.6-Thinking", "code": null}, {"id": 368108, "name": "Segatron-XL large, M=384 + HCP", "code": null}, {"id": 369040, "name": "Segment Anything Model", "code": null}, {"id": 305970, "name": "Self Organizing System", "code": null}, {"id": 371845, "name": "SenseChat 5.5", "code": null}, {"id": 307046, "name": "Seq2Seq LSTM", "code": null}, {"id": 369968, "name": "SexNet classification", "code": null}, {"id": 369967, "name": "SexNet compression", "code": null}, {"id": 306088, "name": "ShuffleNet v1", "code": null}, {"id": 369969, "name": "Siamese-TDNN", "code": null}, {"id": 371534, "name": "SigLIP 400M", "code": null}, {"id": 306135, "name": "SimCLR", "code": null}, {"id": 368352, "name": "SimpleNet", "code": null}, {"id": 368349, "name": "Skywork-13B", "code": null}, {"id": 372367, "name": "Solar Open 100B\n", "code": null}, {"id": 268378, "name": "Sparse all-MLP", "code": null}, {"id": 369529, "name": "Speaker-independent vowel classification", "code": null}, {"id": 306071, "name": "SqueezeNet", "code": null}, {"id": 343968, "name": "Stable Diffusion (LDM-KL-8-G)", "code": null}, {"id": 371971, "name": "Stable Diffusion 3", "code": null}, {"id": 370971, "name": "Stable Diffusion XL (SDXL)", "code": null}, {"id": 368130, "name": "StarCoder", "code": null}, {"id": 306183, "name": "Statement Curriculum Learning", "code": null}, {"id": 371889, "name": "StyleGAN", "code": null}, {"id": 371898, "name": "StyleGAN2", "code": null}, {"id": 371868, "name": "StyleGAN3-R", "code": null}, {"id": 371879, "name": "StyleGAN3-T", "code": null}, {"id": 369004, "name": "Super-vector coding", "code": null}, {"id": 305994, "name": "Support Vector Machines", "code": null}, {"id": 367528, "name": "Swift", "code": null}, {"id": 371330, "name": "Swin Transformer V2 (SwinV2-G)", "code": null}, {"id": 257078, "name": "Switch", "code": null}, {"id": 256997, "name": "System 11", "code": null}, {"id": 371521, "name": "T-NLRv5 XXL", "code": null}, {"id": 257059, "name": "T5-11B", "code": null}, {"id": 257058, "name": "T5-3B", "code": null}, {"id": 371867, "name": "TA-CNN", "code": null}, {"id": 371810, "name": "TC-DNN-BLSTM-DNN", "code": null}, {"id": 370531, "name": "TCAN (WT2)", "code": null}, {"id": 368357, "name": "TCN (P-MNIST)", "code": null}, {"id": 257007, "name": "TD-Gammon", "code": null}, {"id": 370247, "name": "TRPO", "code": null}, {"id": 368046, "name": "TaLK Convolution", "code": null}, {"id": 370521, "name": "Table Tennis Agent", "code": null}, {"id": 371840, "name": "Telechat2-115B", "code": null}, {"id": 371903, "name": "Template Adaptation\n", "code": null}, {"id": 368107, "name": "Tensor-Transformer(1core)+PN (WT103)", "code": null}, {"id": 256993, "name": "Theseus", "code": null}, {"id": 306134, "name": "Theseus 6/768", "code": null}, {"id": 372326, "name": "Tongyi DeepResearch", "code": null}, {"id": 369140, "name": "TrOCR", "code": null}, {"id": 368110, "name": "Tranception", "code": null}, {"id": 257106, "name": "TransE", "code": null}, {"id": 371241, "name": "Transformer (2017)", "code": null}, {"id": 369555, "name": "Transformer (Adaptive Input Embeddings) WT103", "code": null}, {"id": 369007, "name": "Transformer + Simple Recurrent Unit", "code": null}, {"id": 368333, "name": "Transformer - LibriVox + Decoding/Rescoring", "code": null}, {"id": 306111, "name": "Transformer ELMo", "code": null}, {"id": 257098, "name": "Transformer local-attention (NesT-B)", "code": null}, {"id": 369511, "name": "Transformer-XL (257M)", "code": null}, {"id": 368052, "name": "Transformer-XL + RMS dynamic eval", "code": null}, {"id": 368010, "name": "Transformer-XL DeFINE (141M)", "code": null}, {"id": 368013, "name": "Transformer-XL Large + Phrase Induction", "code": null}, {"id": 368106, "name": "TransformerXL + spectrum control", "code": null}, {"id": 371864, "name": "Translation-invariant MLP", "code": null}, {"id": 368087, "name": "TrellisNet", "code": null}, {"id": 369546, "name": "Truck backer-upper", "code": null}, {"id": 368073, "name": "True-Regularization+Finetune+Dynamic-Eval", "code": null}, {"id": 371948, "name": "Turing ULRv5", "code": null}, {"id": 368060, "name": "Turing-NLG", "code": null}, {"id": 371877, "name": "Two Stage Feature Extraction (MNIST)", "code": null}, {"id": 371876, "name": "U-Net", "code": null}, {"id": 368739, "name": "U-PaLM (540B)", "code": null}, {"id": 369023, "name": "UDSMProt", "code": null}, {"id": 306190, "name": "UL2", "code": null}, {"id": 371949, "name": "Unicorn", "code": null}, {"id": 365990, "name": "UnifiedQA", "code": null}, {"id": 366988, "name": "Unsupervised High-level Feature Learner", "code": null}, {"id": 372364, "name": "VAETKI\n", "code": null}, {"id": 369034, "name": "VALL-E", "code": null}, {"id": 368057, "name": "VD-LSTM+REAL Large", "code": null}, {"id": 371883, "name": "VGG-Face", "code": null}, {"id": 257023, "name": "VGG16", "code": null}, {"id": 306053, "name": "VGG19", "code": null}, {"id": 371814, "name": "VILA-13B", "code": null}, {"id": 371528, "name": "VILA1.5-13B", "code": null}, {"id": 368031, "name": "Variational (untied weights, MC) LSTM (Large)", "code": null}, {"id": 369512, "name": "Vector Space Model", "code": null}, {"id": 371482, "name": "Vega v2", "code": null}, {"id": 369193, "name": "ViT-22B", "code": null}, {"id": 306145, "name": "ViT-Base/32", "code": null}, {"id": 368361, "name": "ViT-G (model soup)", "code": null}, {"id": 257085, "name": "ViT-G/14", "code": null}, {"id": 368334, "name": "ViT-G/14 (LiT)", "code": null}, {"id": 306146, "name": "ViT-Huge/14", "code": null}, {"id": 369142, "name": "VideoMAE V2", "code": null}, {"id": 369349, "name": "Volcano 13B", "code": null}, {"id": 371938, "name": "W.A.L.T", "code": null}, {"id": 368347, "name": "W2v-BERT", "code": null}, {"id": 368351, "name": "WeNet (Penn Treebank)", "code": null}, {"id": 369977, "name": "Weight Decay", "code": null}, {"id": 349174, "name": "Whisper", "code": null}, {"id": 257018, "name": "Word2Vec (large)", "code": null}, {"id": 306040, "name": "Word2Vec (small)", "code": null}, {"id": 369192, "name": "XGLM-7.5B", "code": null}, {"id": 306119, "name": "XLM", "code": null}, {"id": 369168, "name": "XLM-RoBERTa", "code": null}, {"id": 306169, "name": "XLMR-XXL", "code": null}, {"id": 306120, "name": "XLNet", "code": null}, {"id": 240142, "name": "Xception", "code": null}, {"id": 306061, "name": "YOLO", "code": null}, {"id": 369177, "name": "YOLOX-X", "code": null}, {"id": 306081, "name": "YOLOv2", "code": null}, {"id": 257041, "name": "YOLOv3", "code": null}, {"id": 368740, "name": "Yi-34B", "code": null}, {"id": 370141, "name": "Yi-Large", "code": null}, {"id": 257093, "name": "Yuan 1.0", "code": null}, {"id": 367497, "name": "Zidong Taichu", "code": null}, {"id": 257006, "name": "Zip CNN", "code": null}, {"id": 368064, "name": "aLSTM(depth-2)+RecurrentPolicy (WT2)", "code": null}, {"id": 368036, "name": "base LM+GNN+kNN", "code": null}, {"id": 306179, "name": "data2vec (language)", "code": null}, {"id": 306178, "name": "data2vec (speech)", "code": null}, {"id": 306177, "name": "data2vec (vision)", "code": null}, {"id": 369151, "name": "eDiff-I", "code": null}, {"id": 368084, "name": "genCNN + dyn eval", "code": null}, {"id": 371847, "name": "gpt-oss-120b", "code": null}, {"id": 371855, "name": "gpt-oss-20b", "code": null}, {"id": 369138, "name": "mPLUG-Owl2", "code": null}, {"id": 369155, "name": "mT0-13B", "code": null}, {"id": 368329, "name": "mT5-XXL", "code": null}, {"id": 371483, "name": "nekomata-14b", "code": null}, {"id": 257073, "name": "wave2vec 2.0 LARGE", "code": null}, {"id": 369010, "name": "xTrimoPGLM -100B", "code": null}]}}, "origins": [{"id": 14136, "title": "Parameter, Compute and Data Trends in Machine Learning", "descriptionSnapshot": "We update this chart with the latest available data from our source every month.\n\nThe authors selected the AI systems for inclusion based on the following necessary criteria:\n\u2014 Have an explicit learning component\n\u2014 Showcase experimental results\n\u2014 Advance the state of the art\n\nIn addition, the systems had to meet at least one of the following notability criteria:\n\u2014 Paper has more than 1000 citations\n\u2014 Historical importance\n\u2014 Important state-of-the-art advance\n\u2014 Deployed in a notable context\n\nThe authors note that: \"For new models (from 2020 onward) it is harder to assess these criteria, so we fall back to a subjective selection. We refer to models meeting our selection criteria as 'milestone models.\"\n", "producer": "Epoch AI", "citationFull": "Epoch AI, \u2018Parameter, Compute and Data Trends in Machine Learning\u2019. Published online at epochai.org. Retrieved from: \u2018https://epoch.ai/data/epochdb/visualization\u2019 [online resource]", "urlMain": "https://epoch.ai/mlinputs/visualization", "urlDownload": "https://epoch.ai/data/epochdb/notable_ai_models.csv", "dateAccessed": "2026-03-07", "datePublished": "2025", "license": {"url": "https://creativecommons.org/licenses/by/4.0/", "name": "CC BY 4.0"}}]}