What Models? Helps Match Local AI Models to Your Hardware
The article discusses a tool that helps users match local AI models to their hardware specifications. Users can input their GPU or VRAM, along with other parameters, to find compatible models. This tool aims to optimize the performance of AI models based on individual system capabilities.
- ▪Users can select their GPU or VRAM to find suitable AI models.
- ▪The tool allows for customization based on minimum context window and tokens per second.
- ▪System RAM can be entered to enable offloading for larger models.
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Record
| Original publisher | What Models? |
| Canonical URL | https://whatmodelscanirun.com/ |
| Publication time | Thu, 21 May 2026 00:14:02 +0000 |
| Retrieval time | 2026-05-21T00:25:03.144Z |
| Last seen | 2026-05-21T00:25:03.144Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | 6ysuWsXFxSP3 |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Select your GPU or VRAM (GB) Minimum context window Any2K4K8K16K32K64K128K200K Minimum tokens/sec Any5 tok/s10 tok/s20 tok/s30 tok/s50 tok/s100 tok/s Required features Any System RAM (optional) ? Enter your system RAM to enable offloading. Models can use system memory to extend context windows or run larger models at reduced speed. GB Pick a GPU or enter VRAM to get started Results Select a GPU or enter your VRAM to see which models you can run.
Excerpt limited to ~120 words for fair-use compliance. The full article is at What Models?.
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