Datasets:

Smeltcore
/

gpu-compatibility

pretty_name: Self-Hosted AI — GPU Compatibility, Recipes and Catalogue
license: cc-by-sa-4.0
language:
  - en
size_categories:
  - 1K<n<10K
task_categories:
  - table-question-answering
  - text-retrieval
tags:
  - gpu
  - vram
  - local-llm
  - quantization
  - hardware-compatibility
  - self-hosted
configs:
  - config_name: compatibility
    default: true
    data_files: data/compatibility.parquet
  - config_name: recipes
    data_files: data/recipes.parquet
  - config_name: models
    data_files: data/models.parquet
  - config_name: gpus
    data_files: data/gpus.parquet
  - config_name: benchmark_sources
    data_files: data/benchmark_sources.parquet

Self-Hosted AI — GPU Compatibility, Recipes and Catalogue

Which open-weight AI models actually run on which consumer GPU, and what it takes to get them running. 2 700 model×GPU verdicts across 100 models and 27 cards, plus 1 009 full setup guides (19 MB of markdown) written against specific hardware.

This is the machine-readable form of smeltcore.com. Every row carries a url back to the page it came from.

Generated 2026-09-24T19:45:21+00:00 from the public read API (https://api.smeltcore.com/api/v1) — no private data, no credentials, reproducible by anyone.

Configs

config	rows	what it is
`compatibility`	2 700	the point of the dataset. One row per model × GPU, with a verdict
`recipes`	1 009	full setup guides, markdown included, tagged by model / GPU / tool
`models`	100	the catalogue: licence, upstream repo, modality
`gpus`	27	the cards: VRAM, vendor, series
`benchmark_sources`	166	third-party measurements, normalised and cited

from datasets import load_dataset

compat = load_dataset("REPO_ID", "compatibility", split="train")
compat.filter(lambda r: r["gpu_slug"] == "rtx-4090" and r["fit"] == "verified")

The `fit` scale

The whole dataset turns on this column, so it is worth reading before using it.

verdict	rows	meaning
`verified`	1 008	somebody ran it on this exact card and wrote down how — there is a recipe behind the row
`fits`	659	inferred: the model's memory floor is under the card's VRAM, and the vendor is supported. Not measured
`unknown`	461	no floor established for this model, so no honest call can be made
`too_big`	572	the memory floor exceeds this card. This is the one verdict asserted from anywhere, not only from same-vendor evidence

The asymmetry between fits and too_big is deliberate: a model is only called runnable on evidence from the same vendor's hardware, but it is called not runnable from a memory floor established anywhere. Being wrong in the optimistic direction wastes somebody's evening; being wrong in the pessimistic direction only costs them a model they could have tried.

min_vram_gb is a filter floor in decimal GB — the smallest card the model is offered on — not a measured peak. Measured peaks, where they exist, are in peak_vram_gb and in benchmark_sources.

Provenance, stated plainly

recipes is first-party. Written for this catalogue against named hardware, with the quantization, runtime and settings each one was written for.

benchmark_sources is not. 111 of 166 rows come from a single third-party site (www.hardware-corner.net); 9 were measured by us, each linking to its raw session in Smeltcore/measurements, and 3 were submitted by readers through the site. It is published as a citation index, not as our benchmarks: what this project contributes is the normalisation — one model slug, one GPU slug, one unit convention — and every row is required to carry source_url back to whoever did the measuring. Credit and verification both belong there. If you use a number from this table, cite the source row, not this dataset.

confidence is a 0–1 score reflecting how much the source is trusted; it is not a statistical confidence interval.

Coverage and what it is not

27 consumer cards — NVIDIA, AMD and Apple silicon. No datacenter GPUs (no H100, no A100): this catalogue is about hardware people own.
8 modalities: llm (38), multimodal (18), image (15), video (14), tts (6), 3d (4), music (3), specialized (2).
Verdicts are about whether it runs, not how well it performs. There is no quality benchmark here and no leaderboard.
The catalogue moves — models get added, quantizations appear weekly. A stale copy of this dataset will understate coverage. generated_at above is the only date that matters.

Licence and attribution

Released under CC BY-SA 4.0, matching the licence the site publishes its data under. Attribution goes to smeltcore.com.

Rows in benchmark_sources describe third-party work; that licence does not extend to the measurements themselves, which belong to the sites named in source_url.

Citation

@misc{smeltcore_selfhosted_ai,
  title  = {Self-Hosted AI — GPU Compatibility, Recipes and Catalogue},
  author = {smeltcore},
  url    = {https://smeltcore.com},
  note   = {Generated 2026-09-24T19:45:21+00:00},
  year   = {2026}
}