🦙 Local LLM - tyler

🧠AI Code

github.com··Hacker News

NexusOS v2.0 – A zero-dependency pipeline streaming server chaos to Parquet

🔌APIs

huggingface.co··Hacker News

Gemma 4 QAT models: Optimizing model compression for mobile and laptop efficiency

🧠AI News Blog

blog.google··Hacker News

Fixing a stuck Ollama runner and building a GPU watchdog

🖥Self-hosting

patrickmccanna.net··Hacker News

GGUF vs GPTQ vs AWQ: The Plain-English Guide to LLM Quantization (and Which One to Pick)

🧠AI

vettedconsumer.com··Hacker News

A system programmer’s guide to LLM inference

📝NLP Blog

blog.xiangpeng.systems··Hacker News

Apples to Apples: MLX vs. Llama.cpp for Gemma 4 12B on an M1 16GB

🧠AI Blog

ziraph.com··Hacker News

Running Two LLMs on a Mini PC Sounds Great Until the Benchmarks Arrive

⚙️Performance Profiling

hackernoon.com·

Integrate on-device AI models into your app using Core AI - WWDC26 - Videos

📊Machine Learning

developer.apple.com··Hacker News

Running Qwen 35B MoE at 450k Context on a Single 32GB GPU

🧠AI

local-llm.utop.workers.dev··Hacker News

Show HN: Ext-Infer

🤖LLMs

infer.displace.tech··Hacker News

zhongkaifu/TensorSharp: A C# inference engine for running large language models (LLMs) locally using GGUF model files. TensorSharp provides a console application, a web-based chatbot interface, and Ollama/OpenAI-compatible HTTP APIs for programmatic access. It supports Windows/MacOS/Linux with full GPU capability

🧠AI Code

github.com··Hacker News

Run (your largest) local models from your iPhone

🍎Apple Blog

lmstudio.ai··Hacker News, r/LocalLLaMA

Google's new Gemma 4 12B model is designed to run on any laptop with 16GB of RAM

🧠AI

arstechnica.com··Hacker News

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

🧠AI Code

github.com··Hacker News

Introducing Gemma 4 12B: a unified, encoder-free multimodal model

🧠AI Blog

blog.google··DEV, Hacker News, r/LocalLLaMA

harshuljain13/llm-inference-at-scale: A Practitioner handbook for production llm serving.

🤖LLMs Code

github.com··Hacker News

zaydmulani09/mnemo: Local-first AI memory layer for any LLM. Persistent knowledge graph, entity extraction, semantic retrieval. Works with Ollama, OpenAI, Anthropic, or any OpenAI-compatible backend.

💾SQLite Code

github.com··Hacker News

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Show HN: Run Llama.cpp In-Process from Java with Project Panama FFM

Token4Token — pay-per-token inference on Gnosis + Swarm

defai-digital/ax-engine: Apple Silicon LLM runtime supporting Gemma 4 and Qwen 3.6 MTP modes

NexusOS v2.0 – A zero-dependency pipeline streaming server chaos to Parquet

Gemma 4 QAT models: Optimizing model compression for mobile and laptop efficiency

Fixing a stuck Ollama runner and building a GPU watchdog

GGUF vs GPTQ vs AWQ: The Plain-English Guide to LLM Quantization (and Which One to Pick)

A system programmer’s guide to LLM inference

Apples to Apples: MLX vs. Llama.cpp for Gemma 4 12B on an M1 16GB

Running Two LLMs on a Mini PC Sounds Great Until the Benchmarks Arrive

Integrate on-device AI models into your app using Core AI - WWDC26 - Videos

Running Qwen 35B MoE at 450k Context on a Single 32GB GPU

Show HN: Ext-Infer

Run (your largest) local models from your iPhone

Google's new Gemma 4 12B model is designed to run on any laptop with 16GB of RAM

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

Introducing Gemma 4 12B: a unified, encoder-free multimodal model

harshuljain13/llm-inference-at-scale: A Practitioner handbook for production llm serving.

zaydmulani09/mnemo: Local-first AI memory layer for any LLM. Persistent knowledge graph, entity extraction, semantic retrieval. Works with Ollama, OpenAI, Anthropic, or any OpenAI-compatible backend.