Skip to main content
Angestrom home
SearchPapersModelsLive AIIntelligence
Search⌕⌘K
EnterprisePricingSign in

Stay Ahead in the AI Revolution

Weekly digest — EPI pulse, top intelligence, fresh lineage. Free, no account.

Follow Angestrom
Global source network
Synced every 5 minutes

Continuous sync from primary AI sources — indexed, enriched, and queryable in real time.

arXivHugging FaceGitHubOpenAIAnthropicDeepMindReutersBBC TechHacker NewsReddit MLVerified feedsFunding
ANGESTROM

The Intelligence Layer of Humanity. Everything AI. All in One Place.

Angestrom connects every piece of the AI ecosystem — data, models, research, companies, tools, and people.

info@angestrom.comwww.angestrom.comLucknow, Uttar Pradesh, India

Product

  • AI Search
  • AI Models
  • Research Papers
  • Companies
  • News & Events
  • GitHub Explorer
  • APIs & Tools
  • Datasets
  • Benchmarks
  • Model lifecycle
  • Funding graph
  • Contributors
  • AI Agents

Resources

  • Weekly digest
  • Documentation
  • Tutorials
  • Guides
  • News
  • Help / Start
  • Community

Company

  • About
  • Contact
  • Privacy Policy
  • Terms of Service
  • Acceptable Use

Enterprise

  • Pricing
  • Workspace
  • Contact Sales

Developer

  • Developer Hub
  • API docs
  • GitHub

Learn

  • Learning Academy
  • Roadmaps
  • Glossary
  • AI for Beginners

Popular Topics

Loading topics…
View All Topics →
© 2026 Angestrom Intelligence Private Limited. All rights reserved.
English
Theme
Angestrom home
SearchPapersModelsLive AIIntelligence
Search⌕⌘K
EnterprisePricingSign in
  1. Home
  2. /Repositories
  3. /john-rocky/apple-silicon-llm-bench
Read original ↗
repoGitHubTrust 82 · PrimaryPublished 1mo agoLive · 29d ago

john-rocky/apple-silicon-llm-bench

Neutral, reproducible benchmark for local LLMs on Apple Silicon (Mac · iPhone · iPad) — MLX, llama.cpp, CoreML, Apple Foundation Models

Lineage graph

Paper → model → repo connections mined from source citations (Tier-1 exact match).

Why these links exist

Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.

  • PossiblePossibly related (embedding) · 50%Ask HN: MacBook vs. Dedicated GPU for LLM →
  • PossiblePossibly related (embedding) · 64%OpenAI and Broadcom announce chip designed for LLM inference at scale →
  • PossiblePossibly related (embedding) · 59%Evaluate a model properly →
  • PossiblePossibly related (embedding) · 48%I mapped which local LLMs actually fit each RAM tier, 8 to 128GB (open dataset) →
  • PossiblePossibly related (embedding) · 48%How're you deploying LLMs in production now-a-days? What's the best and most affordable way? [D] →
  • PossiblePossibly related (embedding) · 50%Gemma 4 12B - MLX Kernel →
  • PossiblePossibly related (embedding) · 46%CPU TTS benchmark with UTMOS MOS scoring: Kokoro, Supertonic, Inflect-Nano, and Kyutai's new Pocket TTS [P] →
  • PossiblePossibly related (embedding) · 51%DynaMiCS: Fine-Tuning LLMs with Performance Constraints Using Dynamic Mixtures - Apple Machine Learning Research →

Covers

newsAsk HN: MacBook vs. Dedicated GPU for LLMnewsOpenAI and Broadcom announce chip designed for LLM inference at scalenewsI mapped which local LLMs actually fit each RAM tier, 8 to 128GB (open dataset)newsHow're you deploying LLMs in production now-a-days? What's the best and most affordable way? [D]

Related to

tutorialEvaluate a model properly

Covers (incoming)

newsGemma 4 12B - MLX KernelnewsCPU TTS benchmark with UTMOS MOS scoring: Kokoro, Supertonic, Inflect-Nano, and Kyutai's new Pocket TTS [P]newsDynaMiCS: Fine-Tuning LLMs with Performance Constraints Using Dynamic Mixtures - Apple Machine Learning ResearchnewsApple M7 Ultra Chip Planned With Up to 1.5 TB of Unified MemorynewsApple M5 isn't making full use of its matmul cores yetnewsSOTA Apple Silicon Inference (August 15, 2026)

Related across the graph

newsOpenAI and Broadcom announce chip designed for LLM inference at scalenewsApple M7 Ultra Chip Planned With Up to 1.5 TB of Unified MemorynewsDynaMiCS: Fine-Tuning LLMs with Performance Constraints Using Dynamic Mixtures - Apple Machine Learning ResearchnewsApple M5 isn't making full use of its matmul cores yetnewsI mapped which local LLMs actually fit each RAM tier, 8 to 128GB (open dataset)newsSOTA Apple Silicon Inference (August 15, 2026)newsCPU TTS benchmark with UTMOS MOS scoring: Kokoro, Supertonic, Inflect-Nano, and Kyutai's new Pocket TTS [P]newsHow're you deploying LLMs in production now-a-days? What's the best and most affordable way? [D]newsAsk HN: MacBook vs. Dedicated GPU for LLMtutorialEvaluate a model properlynewsGemma 4 12B - MLX Kernel
Knowledge path·NOpenAI and Broadcom announce chip designed for LLM inference at scale→NApple M7 Ultra Chip Planned With Up to 1.5 TB of Unified Memory→NDynaMiCS: Fine-Tuning LLMs with Performance Constraints Using Dynamic Mixtures - Apple Machine Learning Research→Rjohn-rocky/apple-silicon-llm-bench

Topics

apple-siliconbenchmarkcoremliosllama-cppllmllm-inferencemacosmlxon-device-ai

Explore

Search similar →Knowledge graph →All repos →Full intelligence feed →
Graph trust82Primary
Graph score52