What's actually the bottleneck for running a vision-language large model on-device? Compute, memory, or bandwidth? The survey at arXiv 2607.20981 gives a counter-intuitive answer: it's none of them in isolation — it's the problem that emerges only when they act on each other. Jay Gor and five co-authors map six previously separately optimized technology lines — visual-token compression, KV cache optimization, MoE routing, low-bit quantization, and edge deployment — onto the same diagram. They point out that these optimizations are never orthogonal: visual-token compression shifts the downstream feature distribution and breaks MoE routing decisions; quantized router logits in turn cause expert allocation to drift; KV cache eviction policy directly determines how much multimodal evidence is retained; and hardware constraints often re-capture the gains from "saved compute" as new memory and communication bottlenecks. This cross-effect means that papers claiming "4x compression, lossless" or "2-bit quantization, almost no drop" on a single-point metric will likely need to be discounted when placed inside an end-to-end pipeline. The survey's new diagnostic metric, Temporal Routing Consistency, is designed to detect whether a video MoE model's routing stays stable over the temporal dimension — a health signal that almost nobody has monitored before, but one that's critical for long-video understanding. The engineering trade-off map is redrawn from here: accuracy vs. token budget, static vs. adaptive compression, sparse-routing efficiency vs. expert collapse, low-bit inference vs. modality-specific degradation — these trade-offs can no longer live in their own silos. The survey closes by pointing out four open directions: routing-aware compression, cross-modal cache management, hardware-aware co-design, and a unified edge-intelligence benchmark. Bottom line: on-device multimodal LLMs aren't a "pick the fastest algorithm and you're done" problem — they're a joint-design engineering problem. The dividends of single-point optimization are being eaten away bite by bite by the interlocked system cost. For teams working on end-cloud collaboration, device-level Agents, in-vehicle or on-robot local inference, it's worth drawing out the whole closed-loop diagram first, before picking which optimization path to take.