Good article about local LLM on MacBook Air
The author of the article switched to Ollama's new MLX engine for running local LLMs on their MacBook Air and noticed significant performance improvements. The new engine makes better use of Apple Silicon and unified memory architecture, reducing unnecessary memory movement during inference. The author's overall experience with local LLMs on their Mac has improved, with faster inference and more responsive performance.
- ▪Ollama's new MLX engine improves performance for local LLMs on Macs by making better use of Apple Silicon and unified memory architecture.
- ▪The updated engine reduces inference overhead and improves GPU-backed sampling, allowing for faster token generation.
- ▪The author noticed a significant improvement in performance, with inference speeds almost twice as fast as before.
Hacker News (AI / LLM) files mainly under ai. We currently carry 3,262 of its stories.
Story provenance
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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 | XDA |
| Canonical URL | https://www.xda-developers.com/ollama-new-mlx-engine-local-llm-mac-twice-fast/ |
| Publication time | Tue, 30 Jun 2026 08:23:07 +0000 |
| Retrieval time | 2026-06-30T08:34:23.979Z |
| Last seen | 2026-06-30T08:34:23.979Z |
| 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 | ZhXFdVZqUtvY |
| 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
{ "@context": "https://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": "1", "name": "Home", "item": "https://www.xda-developers.com/" }, { "@type": "ListItem", "position":"2", "name": "AI tools", "item": "https://www.xda-developers.com/ai-tools/" }, { "@type": "ListItem", "position":"3", "name": "I switched my local LLM setup to Ollama's new MLX engine, and my Mac suddenly feels twice as fast", "item": "https://www.xda-developers.com/ollama-new-mlx-engine-local-llm-mac-twice-fast/" } ] } I switched my local LLM setup to Ollama's new MLX engine, and my Mac suddenly feels twice as fast By Anurag Singh Published Jun 27, 2026, 7:30 AM EDT Anurag is an experienced journalist and author who’s been covering tech for the past 5 years, with a focus…
Excerpt limited to ~120 words for fair-use compliance. The full article is at XDA.
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