Category archive
Images, audio and a device budget change the whole pipeline.
Text pipelines assume input is cheap to read and cheap to store. Documents, audio and images break both assumptions, and a phone breaks them again with a memory ceiling that cannot be scaled out of. These articles work through what survives those constraints.
Articles in Multimodal and Edge
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Multimodal AI Pipelines for Images, Audio, and Documents
Engineering preprocessing, temporal and spatial grounding, context budgets, validation, and storage around multimodal models.
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Edge LLM Inference: Designing for the Device
Quantization, memory, thermal budgets, runtimes, privacy, and hybrid execution for useful models on constrained hardware.
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Put this against a real system.
If a decision in Multimodal and Edge is in front of you right now, the fastest version of this is a call: bring the architecture, the failure you are seeing, and the constraint you cannot move.
Discuss the system
