Running a small LLM in reMarkable 2
unremarkable A tiny LLM running entirely on a reMarkable 2. An inference engine written in C++23, built to learn how to make language models run faster on limited hardware. It starts as plain FP32 loops and gets faster one measured optimization at a time; each step is tagged (rung-01, rung-02, …) and explained in the optimization ladder.
- ▪unremarkable A tiny LLM running entirely on a reMarkable 2.
- ▪An inference engine written in C++23, built to learn how to make language models run faster on limited hardware.
- ▪It starts as plain FP32 loops and gets faster one measured optimization at a time; each step is tagged (rung-01, rung-02, …) and explained in the optimization ladder.
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Story provenance
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Record
| Original publisher | GitHub |
| Canonical URL | https://github.com/ykumards/unremarkable |
| Publication time | Mon, 14 Sep 2026 16:20:11 +0000 |
| Retrieval time | 2026-09-14T16:21:51.383Z |
| Last seen | 2026-09-14T16:21:51.383Z |
| 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 | snoCf8KnfMR5 · 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
unremarkable A tiny LLM running entirely on a reMarkable 2. An inference engine written in C++23, built to learn how to make language models run faster on limited hardware. It starts as plain FP32 loops and gets faster one measured optimization at a time; each step is tagged (rung-01, rung-02, …) and explained in the optimization ladder. Runs SmolLM2-135M-Instruct, with optional tablet UI patches. Ceiling SmolLM2-135M, Q8, two cores: 2.708 GB/s ÷ 0.151 GB/token ≈ 17.9 tok/s memory-only ceiling. Calculation Benchmarks and changes Batched prefill cuts the wait for the first token from 3.94 s to 2.64 s on our 26-token prompt. Runs -- the turtle tracks decode speed. Code and docs Inference: follow a token through memory and the model.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.