Datasets:
File size: 2,810 Bytes
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license: cc-by-4.0
language:
- en
- ru
tags:
- gpu
- hardware
- nvidia
- amd
- benchmarks
- semiconductors
pretty_name: "GPU Ark — GPU specifications & benchmarks (13.5k GPUs, 1999–2025)"
size_categories:
- 10K<n<100K
source_datasets:
- original
configs:
- config_name: gpu_specs
data_files: gpuark-gpu-specs.csv
- config_name: benchmarks
data_files: gpuark-benchmarks.csv
---
# GPU Ark — open GPU specifications & benchmarks dataset
Specifications of **13,566 GPUs** released between 1999 and 2025 — from the GeForce 256 to
NVIDIA Blackwell and AMD Instinct MI355X — plus **993 third-party benchmark results**.
Curated and maintained by **[GPU Ark](https://gpuark.com/)** (a GPU catalog & price comparison
project). Canonical source and always-fresh copy: **<https://gpuark.com/datasets/>**.
## Files
| File | Rows | What |
|------|-----:|------|
| `gpuark-gpu-specs.csv` | 13,566 | One row per GPU — public spec columns |
| `gpuark-benchmarks.csv` | 993 | Third-party benchmark results, join on `gpu_id` |
| `gpuark-gpu-dataset.sqlite` | — | Both tables (`gpu_specs`, `benchmarks`) for SQL |
### Key columns (`gpu_specs`)
`id, name, slug, vendor (nvd/amd/int), manufacturer, arch_name, card_release_date,
proc_foundry, proc_process_size, proc_transistors, cores, tensor_cores, base_clock,
boost_clock, ram, ram_type, bus_width, ram_bandwidth, fp16/fp32/fp64/bf16/tf32/int8_performance,
tdp, multi_gpu, api_cuda, is_retail_board, gpi_value`.
## Quick start
```python
import pandas as pd
df = pd.read_csv("gpuark-gpu-specs.csv", parse_dates=["card_release_date"])
nv = df[df.vendor == "nvd"]
# peak FP32 flagship per year
print(nv.groupby(nv.card_release_date.dt.year).fp32_performance.max())
```
## Known issues (read before drawing conclusions)
- **`vendor`** is set for ~2,360 of ~13,566 rows (`nvd`/`amd`/`int`); the rest are mostly
partner/OEM board variants without a chip-vendor tag. Filter on `vendor` for vendor-level work.
- **`ram` (VRAM)** unit is inconsistent across eras — older cards store MB, newer store GB
(a value ≥ 256 on a pre-2018 card is almost certainly MB).
- **`fp16`/`bf16`/`int8`** are sparse and not consistently tensor-vs-non-tensor across vendors
(NVIDIA Ampere+ tensor figures are often listed *with* structured sparsity = 2× dense).
Don't compare low-precision peaks cross-vendor without checking the card.
- **`card_release_date`** has a handful of implausible years — filter to 1998..2025.
- **`is_retail_board=True`** = AIB/OEM editions of a reference chip (near-duplicates).
## License & attribution
**CC BY 4.0** — free to use with attribution to **[gpuark.com](https://gpuark.com/)**.
## Citation
```
GPU Ark (2026). GPU specifications & benchmarks dataset. https://gpuark.com/datasets/
```
|