Datasets:
File size: 4,284 Bytes
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license: cc-by-4.0
task_categories:
- tabular-regression
tags:
- webgpu
- gpu
- benchmark
- browser
- compute-shaders
pretty_name: WebGPU Compute Benchmarks Across Browsers and GPU Vendors
size_categories:
- 1K<n<10K
configs:
- config_name: benchmark_runs
data_files: benchmark_runs.csv
- config_name: transformer_runs
data_files: transformer_runs.csv
---
# WebGPU compute benchmarks across browsers and GPU vendors
Every run submitted to [gpubench.dev](https://gpubench.dev) between 2026-03-30
and 2026-08-14, exported from the live table. WebGPU compute-shader throughput
measured in real browsers on whatever hardware visitors happened to have.
This exists because two preprints cite cross-vendor results from this table and
the table is live and mutable, so those claims could not be checked by a reader.
This snapshot is the checkable version.
## Files
| File | Rows | What |
|---|---|---|
| `benchmark_runs.csv` | 794 | Five compute workloads — Rastrigin, N-body, Monte Carlo Pi, Acrobot, MountainCar, CartPole — plus a composite score |
| `transformer_runs.csv` | 380 | Fused vs unfused transformer decoding at D=32/64/128, L=1/4, with per-configuration speedups |
## Coverage
| Vendor | Runs | | OS | Runs |
|---|---:|---|---|---:|
| nvidia | 229 | | Windows | 342 |
| apple | 209 | | macOS | 225 |
| Unknown | 101 | | Linux | 215 |
| amd | 83 | | Android | 11 |
| qualcomm | 59 | | ChromeOS | 1 |
| intel | 56 | | | |
| arm | 35 | | | |
| google | 16 | | | |
| img-tec | 6 | | | |
## How many devices
There is no device identifier in this table, so "unique devices" depends on how
you define it. Three defensible answers:
| Definition | Count |
|---|---:|
| distinct `gpu_name` | 34 |
| distinct (`gpu_name`, `browser`, `os`) | 119 |
| distinct (`gpu_name`, `os`, screen size) | 174 |
Pick the one that suits the question and say which. Earlier material from this
project published 592 and 92; neither corresponds to any of these, and both
should be disregarded.
## Caveats that matter
**`max_buffer` is not a device capability.** It holds 268435456 for all 794
rows, which is WebGPU's default `maxBufferSize`. The benchmark page never
requested raised limits, so the column records what was asked for rather than
what the adapter could offer.
**`gpu_name` is adapter-reported and coarse.** Chrome masks device strings, so
values are architecture-level (`apple metal-3`, `nvidia lovelace`) and 101 rows
report `Unknown GPU`. One value is a raw vendor id (`0x2bb1`).
**No deduplication.** A visitor who ran the benchmark repeatedly contributes
several rows. There is nothing here to identify or group them, by design.
**Uncontrolled conditions.** Thermal state, competing load, browser version,
and power mode are unknown per row. Treat cross-vendor comparisons as
indicative, not as controlled measurements.
**`score` is a composite** of the workload throughputs, useful for ranking
within this dataset and not comparable to any other benchmark.
## Schema
`benchmark_runs` carries 59 columns: identity (`id`, `created_at`), device
(`gpu_name`, `gpu_vendor`, `gpu_arch`, `backend`, `os`, `browser`, `is_mobile`,
screen size, `device_pixel_ratio`), limits (`max_buffer`, `features`,
`max_workgroup_x/y/z`, `max_invocations`), and per-workload throughput in
generations per second with `mean`/`min`/`max`/`std` where the harness recorded
repeats. `bench_version` distinguishes harness revisions; `*_batched_gps`
columns appear only in later runs.
`transformer_runs` carries 23: the same device fields plus `config`, `layers`,
`d_model`, `dispatches`, timings (`unfused_ms`, `fused_1t_ms`, `parallel_ms`,
`unfused_batched_ms`), derived speedups, `tokens_per_sec`, and `equiv_max_diff`
— the numerical difference between fused and unfused output, which is the
correctness check on the fusion.
## Privacy
No identifiers of any kind. `id` is a random UUIDv4 per submission. No IP
addresses, accounts, or user-supplied text are collected. Device strings come
from the WebGPU adapter and the user-agent, both already exposed to any page.
## Source
Collected by [gpubench.dev](https://gpubench.dev)
([source](https://github.com/abgnydn/gpubench)). Related work:
[zerotvm.com](https://zerotvm.com) and
[kernelfusion.dev](https://kernelfusion.dev).
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