Show HN: Offline 180.9M-parameter LLM and Agent inference on ESP32-P4
PFor 中文文档 PLE-MoE-W1.58A8 Architecture PFor is an LLM running on ESP32-P4, although technically it should be called an SLM. It has Instruct(ChatML) and Agent capabilities, despite both being extremely early and highly unstable. Its inference speed on ESP32-P4 is about 9 tokens/s.
- ▪PFor 中文文档 PLE-MoE-W1.58A8 Architecture PFor is an LLM running on ESP32-P4, although technically it should be called an SLM.
- ▪It has Instruct(ChatML) and Agent capabilities, despite both being extremely early and highly unstable.
- ▪Its inference speed on ESP32-P4 is about 9 tokens/s.
Hacker News (AI / LLM) files mainly under ai. We currently carry 3,739 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | GitHub |
| Canonical URL | https://github.com/cyfrit/p-for-llm |
| Publication time | Wed, 05 Aug 2026 14:56:34 +0000 |
| Retrieval time | 2026-08-05T15:15:44.720Z |
| Last seen | 2026-08-05T15:15:44.720Z |
| 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 | 2W-1Xxk11TjD · 1 stories |
| 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
PFor 中文文档 PLE-MoE-W1.58A8 Architecture PFor is an LLM running on ESP32-P4, although technically it should be called an SLM. It has Instruct(ChatML) and Agent capabilities, despite both being extremely early and highly unstable. Its inference speed on ESP32-P4 is about 9 tokens/s. I use the WT9932P4-Tiny development board (I bought mine on Taobao for CNY 39.9, about USD 6; it is around USD 10 on AliExpress), which has 32 MB PSRAM and 16 MB Flash. The USB connection admittedly makes "offline" look somewhat questionable. The board's Flash cannot hold the weights loaded into PSRAM, so they are transferred over USB at startup; the host performs no inference. Give it an SD card for the weights, and it can run entirely without a host.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.