0xSero builds a first-principles picture of local AI from hardware and model weights through inference engines to agents and practical tasks. Memory capacity determines what can fit, while compute, memory bandwidth and the way a workload is scheduled influence responsiveness. The discussion contrasts conventional graphics cards with larger-capacity integrated-memory systems rather than treating memory size alone as a measure of speed.
0xSero describes prefill, decoding and the software that connects model architectures to hardware. Examples include moving weights between GPU memory, system memory and storage, splitting work across devices, and placing frequently used model components on faster hardware. Reported speeds are observations from particular experimental configurations, not universal benchmarks or guaranteed purchase outcomes.
0xSero demonstrates local agents attempting browser-based drawing and a small interactive scene, and discusses background asset generation and security research as possible workloads. The demonstrations also expose limits: concurrent sessions compete for resources, reasoning can add latency, and a browser task can be disrupted by manual interaction. The practical aim is to connect available hardware to a useful task and improve the configuration through measurement.
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