The Apple Neural Engine Inference Book
The Apple Neural Engine Inference Book serves as a comprehensive guide for practitioners working with production inference on Apple's Neural Engine. It covers various topics including CoreML, Swift runtimes, and model validation. The book includes chapters on empirical rules, porting recipes, quantization, and more.
- ▪The book provides a detailed overview of production inference on the Apple Neural Engine.
- ▪It includes chapters on topics such as quantization, shard sizing, and stateful KV cache.
- ▪The source code and additional resources are available in the ane-book repository.
Hacker News (Newest) files mainly under programming. We currently carry 5,306 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
inspect →
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 | Alvaro-videla |
| Canonical URL | https://alvaro-videla.com/ane-book/ |
| Publication time | Fri, 29 May 2026 14:11:03 +0000 |
| Retrieval time | 2026-05-29T14:20:01.346Z |
| Last seen | 2026-05-29T14:20:01.346Z |
| 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 | Psy7Qhf7cKl1 |
| 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
The Apple Neural Engine Inference Book A practitioner’s guide to production inference on the Apple Neural Engine with CoreML, Swift runtimes, ANE-only residency checks, and validated model manifests. By Alvaro Videla - @old_sound Chapters Chapter Topic 00 - Modern Inference Tokens, prefill/decode, KV cache, ANE vs GPU vs CPU, the Conv2d trick 01 - ANE Laws Empirical rules: shard limits, quantization, residency 02 - Porting Recipe GGUF to CoreML, step by step 03 - Quantization INT8 production, INT4 tradeoffs, the silent CPU fallback 04 - Shard Sizing Layer count vs size, 250 MB limit, LM-head splits 05 - Stateful KV Cache MLState, Swift daemon design, decode loop 06 - RangeDim + Speculative Variable T, n-gram acceptance 07 - MoE on ANE Soft routing, per-expert dispatch, ZAYA and Privacy…
Excerpt limited to ~120 words for fair-use compliance. The full article is at Alvaro-videla.
Discussion
0 commentsMore from Alvaro-videla
