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+ ---
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+ license: cc-by-4.0
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+ language:
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+ - en
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+ - ru
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+ tags:
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+ - gpu
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+ - hardware
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+ - nvidia
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+ - amd
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+ - benchmarks
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+ - semiconductors
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+ pretty_name: "GPU Ark — GPU specifications & benchmarks (13.5k GPUs, 1999–2025)"
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+ size_categories:
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+ - 10K<n<100K
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+ source_datasets:
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+ - original
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+ configs:
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+ - config_name: gpu_specs
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+ data_files: gpuark-gpu-specs.csv
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+ - config_name: benchmarks
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+ data_files: gpuark-benchmarks.csv
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+ ---
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+
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+ # GPU Ark — open GPU specifications & benchmarks dataset
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+
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+ Specifications of **13,566 GPUs** released between 1999 and 2025 — from the GeForce 256 to
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+ NVIDIA Blackwell and AMD Instinct MI355X — plus **993 third-party benchmark results**.
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+ Curated and maintained by **[GPU Ark](https://gpuark.com/)** (a GPU catalog & price comparison
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+ project). Canonical source and always-fresh copy: **<https://gpuark.com/datasets/>**.
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+
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+ ## Files
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+
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+ | File | Rows | What |
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+ |------|-----:|------|
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+ | `gpuark-gpu-specs.csv` | 13,566 | One row per GPU — public spec columns |
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+ | `gpuark-benchmarks.csv` | 993 | Third-party benchmark results, join on `gpu_id` |
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+ | `gpuark-gpu-dataset.sqlite` | — | Both tables (`gpu_specs`, `benchmarks`) for SQL |
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+
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+ ### Key columns (`gpu_specs`)
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+ `id, name, slug, vendor (nvd/amd/int), manufacturer, arch_name, card_release_date,
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+ proc_foundry, proc_process_size, proc_transistors, cores, tensor_cores, base_clock,
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+ boost_clock, ram, ram_type, bus_width, ram_bandwidth, fp16/fp32/fp64/bf16/tf32/int8_performance,
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+ tdp, multi_gpu, api_cuda, is_retail_board, gpi_value`.
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+
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+ ## Quick start
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+
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+ ```python
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+ import pandas as pd
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+ df = pd.read_csv("gpuark-gpu-specs.csv", parse_dates=["card_release_date"])
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+ nv = df[df.vendor == "nvd"]
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+ # peak FP32 flagship per year
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+ print(nv.groupby(nv.card_release_date.dt.year).fp32_performance.max())
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+ ```
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+
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+ ## Known issues (read before drawing conclusions)
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+
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+ - **`vendor`** is set for ~2,360 of ~13,566 rows (`nvd`/`amd`/`int`); the rest are mostly
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+ partner/OEM board variants without a chip-vendor tag. Filter on `vendor` for vendor-level work.
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+ - **`ram` (VRAM)** unit is inconsistent across eras — older cards store MB, newer store GB
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+ (a value ≥ 256 on a pre-2018 card is almost certainly MB).
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+ - **`fp16`/`bf16`/`int8`** are sparse and not consistently tensor-vs-non-tensor across vendors
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+ (NVIDIA Ampere+ tensor figures are often listed *with* structured sparsity = 2× dense).
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+ Don't compare low-precision peaks cross-vendor without checking the card.
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+ - **`card_release_date`** has a handful of implausible years — filter to 1998..2025.
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+ - **`is_retail_board=True`** = AIB/OEM editions of a reference chip (near-duplicates).
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+
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+ ## License & attribution
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+
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+ **CC BY 4.0** — free to use with attribution to **[gpuark.com](https://gpuark.com/)**.
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+
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+ ## Citation
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+
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+ ```
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+ GPU Ark (2026). GPU specifications & benchmarks dataset. https://gpuark.com/datasets/
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+ ```