Inference optimization
9 articles
Distributed computingFrom idle capacity to served inference: what operators need
Inference demand rises and falls through the day, so most AI capacity has spare hours. This article covers the five things a data-centre operator needs to sell those hours on an open network: machine identity, a trusted model supply, metering, proof of availability and direct settlement.
5 min read
Generative mediaGenerative Media Pipelines: Right Hardware, Pay Per Result
Image, video and audio generation have cost profiles unlike chat. How to route each job to hardware that fits it, meter it in units that match the work, check licence territory and mark outputs for provenance.
5 min read
Inference optimizationInference Optimisation From Consumer GPUs to the Data Centre
Quantisation, speculative decoding, continuous batching, paged and reused KV cache, and prefill/decode disaggregation: what the 2025-2026 work shows, and what each technique means when the GPUs belong to many operators on a wide-area network.
6 min read
Model routingModel Ladders: Answer With Small Models, Escalate Only When Needed
Most requests do not need the largest model. What the research on cascades and routing actually shows, where it falls short, and how Tenzro routes by measured difficulty and closes the loop on escalation.
5 min read
RetrievalRetrieval at the Source: Embeddings and Search Near the Data
Retrieval pipelines copy an organisation's documents into embeddings, indexes and caches, often far from where the data lives. Why embeddings are sensitive data, how to size them, and an architecture that keeps retrieval close to the source.
4 min read
Model routingRouting Across Many Providers, and Why Receipts Must Be Verifiable
A model router chooses on price, latency and standing, hedges the slow tail and fails over in seconds. None of that is trustworthy unless each answer comes with a receipt a third party can check. How Tenzro does both.
5 min read
Enterprise AITotal Cost of AI Ownership: What Inference Really Costs
Token prices are the visible part of an AI bill. A cost model for enterprise inference that includes the harness around the model, utilisation risk and supplier lock-in, and seven levers that reduce it.
5 min read
AI factoriesUtilisation decides AI factory economics
An AI factory's accelerators cost the same whether they are busy or idle, so served work per installed accelerator is the number that matters. This article covers where utilisation is lost, and how matching demand across many operators recovers some of it.
5 min read
Voice AIVoice AI Latency Budgets: Serve Speech Close to the Speaker
People expect a reply within a few hundred milliseconds. A latency budget for voice agents, where each millisecond goes, and why speech recognition and synthesis belong near the user while the language model can sit further away.
4 min read