LLM inference spans the full stack from VRAM constraints and quantization choices on consumer hardware to latency optimization in production agent services, with tooling debates about transparency, local runtimes, and cost-efficient alternatives to large models.

Multimodal AI systems process and generate across multiple input and output types, including text, images, audio, and video; recent advances show these models getting smaller, faster, and embedded in production tooling.

RAG grounds LLM outputs in external documents at query time, but its limitations around cross-document synthesis have pushed practitioners toward alternatives like compiled knowledge bases that pre-synthesize information into structured, queryable Markdown.