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Model Distillation: Small AI With a Big Model’s Knowledge
How companies use model distillation to build small, local AI models that rival the big ones—faster, cheaper, and inside their own walls.
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Agentic RAG: When AI Decides for Itself What to Look Up
Why a planning, multi-step retrieval routine answers complex questions with evidence – and where the extra effort pays off.
Multi-LoRA: Run Dozens of Fine-Tuned AI Models on One GPU
One fine-tuned AI per team, but only one GPU? Multi-LoRA serving makes dozens of model variants affordable on a single card.
Reranking: Why RAG Systems Need a Second Look at Their Hits
Vector search finds the right passage but rarely at the top. Reranking with cross-encoders reorders RAG hits for sharper, more reliable answers.
Speculative Decoding: Make Self-Hosted AI Noticeably Faster
Speculative decoding speeds up self-hosted AI models with no loss of quality. How it works and what it means for your budget.
Inference Servers: How vLLM Makes Self-Hosted AI Pay Off
Continuous batching, PagedAttention, quantization: how inference servers saturate the GPU and make self-hosted AI economical.
Semantic Caching: Reuse Your AI Answers Smartly in 2026
Semantic caching cuts your AI costs and response times: the system spots similar questions and instantly returns a vetted answer again.