Tracking Compute-Intensive AI Models
@owid.epoch_compute_intensive
@owid.epoch_compute_intensive
A dataset that tracks compute-intensive AI models, with training compute over 10²³ floating point operations (FLOP). This corresponds to training costs of hundreds of thousands of dollars or more.
To identify compute-intensive AI models, the team at Epoch AI used various resources, estimating compute when not directly reported. They included benchmarks and repositories, such as Papers With Code and Hugging Face, to find models exceeding 10²³ FLOP. They also explored non-English media and specific leaderboards, particularly focusing on Chinese sources.
Additionally, they examined blog posts, press releases from major labs, and scholarly literature to track new models. A separate table was created for models with unconfirmed but plausible compute levels. Despite thorough methods, proprietary and secretive models may have been missed.
CREATE TABLE owid_epoch_compute_intensive_1 (
"days_since_1949" INTEGER,
"system" VARCHAR,
"publication_date" TIMESTAMP,
"domain" VARCHAR,
"organization_categorization" VARCHAR,
"parameters" BIGINT,
"training_dataset_size_datapoints" BIGINT -- Training Dataset Size, Datapoints,
"training_computation_petaflop" FLOAT
);CREATE TABLE owid_epoch_compute_intensive_2 (
"days_since_1949" INTEGER,
"system" VARCHAR,
"publication_date" TIMESTAMP,
"domain" VARCHAR,
"parameters" BIGINT,
"training_dataset_size_datapoints" FLOAT -- Training Dataset Size, Datapoints,
"training_computation_petaflop" FLOAT
);CREATE TABLE owid_epoch_compute_intensive_3 (
"days_since_1949" INTEGER,
"system" VARCHAR,
"publication_date" TIMESTAMP,
"domain" VARCHAR,
"parameters" BIGINT,
"training_dataset_size_datapoints" FLOAT -- Training Dataset Size, Datapoints,
"training_computation_petaflop" FLOAT
);CREATE TABLE owid_epoch_compute_intensive_4 (
"days_since_1949" INTEGER,
"system" VARCHAR,
"publication_date" TIMESTAMP,
"domain" VARCHAR,
"parameters" BIGINT,
"training_dataset_size_datapoints" FLOAT -- Training Dataset Size, Datapoints,
"training_computation_petaflop" FLOAT
);CREATE TABLE owid_epoch_compute_intensive_5 (
"days_since_1949" INTEGER,
"system" VARCHAR,
"publication_date" TIMESTAMP,
"domain" VARCHAR,
"parameters" BIGINT,
"training_dataset_size_datapoints" BIGINT -- Training Dataset Size, Datapoints,
"training_computation_petaflop" FLOAT
);CREATE TABLE owid_epoch_compute_intensive_6 (
"days_since_1949" INTEGER,
"model" VARCHAR,
"publication_date" TIMESTAMP,
"domain" VARCHAR,
"parameters" BIGINT,
"training_dataset_size_datapoints" BIGINT -- Training Dataset Size, Datapoints,
"training_computation_petaflop" FLOAT
);CREATE TABLE owid_epoch_compute_intensive_7 (
"days_since_1949" INTEGER,
"model" VARCHAR,
"publication_date" TIMESTAMP,
"domain" VARCHAR,
"parameters" BIGINT,
"training_dataset_size_datapoints" BIGINT -- Training Dataset Size, Datapoints,
"training_computation_petaflop" FLOAT
);CREATE TABLE owid_epoch_compute_intensive_8 (
"days_since_1949" INTEGER,
"model" VARCHAR,
"publication_date" TIMESTAMP,
"domain" VARCHAR,
"parameters" BIGINT,
"training_dataset_size_datapoints" BIGINT -- Training Dataset Size, Datapoints,
"training_computation_petaflop" FLOAT
);CREATE TABLE owid_epoch_compute_intensive_9 (
"days_since_1949" INTEGER,
"model" VARCHAR,
"publication_date" TIMESTAMP,
"domain" VARCHAR,
"parameters" BIGINT,
"training_dataset_size_datapoints" BIGINT -- Training Dataset Size, Datapoints,
"training_computation_petaflop" FLOAT
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