Smeltcore/gpu-compatibility

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

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.

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}
}