\newcommand{\WPabstract}{%
Sparse mixture-of-experts (MoE) language models touch only a few experts per token, so a model whose weights exceed a
machine's memory can still be served if the right experts are in the right place at the right time. This working paper
reports the design, implementation and measurement of \titan{}, an MLX-native inference runtime for Apple Silicon that
manages expert residency with bounded per-layer slot tables, loads experts on demand from local NVMe, prefetches them with
causal predictors, and (in its distributed mode) executes them on other Macs of a heterogeneous cluster connected by
gigabit Ethernet. Every design choice is validated against mlx-lm on identical weights: with all experts resident the runtime is
bit-identical in logits and within one percent of mlx-lm's decode throughput; with bounded residency the router's choices and
the logits are unchanged by construction. We state five hypotheses (causal prefetch, topology-aware placement, hierarchical
caching, fused Metal kernels, and adaptive orchestration on the latency--throughput--energy frontier) and test each one with a
pre-registered protocol on a live inventory of a 21-node Mac cluster. This version (0.1) is a \emph{protocol and preliminary}
working paper: it contains the verified hardware inventory, the prior-art audit, the single-node runtime and its correctness
evidence, and the first controlled single-node experiments; distributed and energy experiments are reported only where their
raw logs exist, and pending experiments are explicitly labelled as such.%
}