Technology · Machine Learning Systems
llama.cpp in the Real World: On-Device LLM Inference, Profiling, and Deployment
by Trex Team
Large language models no longer live only in hyperscale data centers. This rigorous engineering guide treats on-device inference as a discipline shaped by real hardware limits: it explains how llama.cpp is structured as an inference stack, why the GGUF format defines the deployment boundary, and how backend selection, build strategy, quantization, context sizing, and KV-cache planning interact to decide whether a model is feasible -- and how fast it runs -- on the machine in front of you.
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