Show HN: All local LLM(s) on all Apple Devices
How it worksWriting speed is set by the memory bandwidthTo write each token, the Mac reads every active weight of the model once. So the writing speed (decode) is about the memory bandwidth divided by the size of those weights. An M5 Max reads 614 GB per second: a dense 70B model at 4 bits weighs about 40 GB, so it writes about 13 tokens per second.The simulator uses 75 to 87% of the bandwidth with MLX depending on the quantization (5 points less with llama.cpp), plus a fixed cost per token measured on each kind of chip and model.
- ▪How it worksWriting speed is set by the memory bandwidthTo write each token, the Mac reads every active weight of the model once.
- ▪So the writing speed (decode) is about the memory bandwidth divided by the size of those weights.
- ▪An M5 Max reads 614 GB per second: a dense 70B model at 4 bits weighs about 40 GB, so it writes about 13 tokens per second.The simulator uses 75 to 87% of the bandwidth with MLX depending on the quantization (5 points less with llama.cpp),
Hacker News (AI / LLM) files mainly under ai. We currently carry 7,676 of its stories.
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Story provenance
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Record
| Original publisher | Simulkit |
| Canonical URL | https://simulkit.com/en/mac-local-llm |
| Publication time | Mon, 05 Oct 2026 20:55:29 +0000 |
| Retrieval time | 2026-10-05T21:10:43.132Z |
| Last seen | 2026-10-05T21:10:43.132Z |
| 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 | WEsZmxTVEyCo · 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
How it worksWriting speed is set by the memory bandwidthTo write each token, the Mac reads every active weight of the model once. So the writing speed (decode) is about the memory bandwidth divided by the size of those weights. An M5 Max reads 614 GB per second: a dense 70B model at 4 bits weighs about 40 GB, so it writes about 13 tokens per second.The simulator uses 75 to 87% of the bandwidth with MLX depending on the quantization (5 points less with llama.cpp), plus a fixed cost per token measured on each kind of chip and model. A long conversation slows things down: every new token also reads the KV cache, the model's memory of the context.Reading speed is set by the GPUBefore answering, the model reads your prompt (prefill).
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Simulkit.
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