May 1, 20264 min read
Edge visual-language systems do not have one universal bottleneck. End-to-end latency depends on the vision encoder, projector, language-model prefill, runtime, precision, input resolution, and target hardware.
This article presents Cache Cliff as my terminology for the non-linear performance changes that can appear when a model, runtime, or memory configuration crosses a hardware constraint. It is an analytical framing, not a claim that one cache threshold explains every device or workload.
The analysis is informed by public VLM and edge-inference benchmarking tools and literature, including vLLM's benchmark tooling and PolyThrottle. Their results are not interchangeable: a reported number only applies to its stated model, device, resolution, precision, runtime, and workload.
For that reason, this project does not present aggregated figures as personal measurements. Any deployment decision should be validated on the intended hardware with the full pipeline measured separately: image preprocessing, vision encoding, projection, prefill, and decoding.
The practical question is not “which encoder is best?” but “which encoder-runtime-hardware combination satisfies this product’s accuracy, latency, memory, and power constraints?” A sudden regression can come from cache and memory pressure, unsupported operators, data movement, thermal behaviour, or runtime scheduling. Cache Cliff is a useful prompt to investigate those boundaries rather than interpolate from a single benchmark.
This work was presented at the International Conference on Computing and Communication Networks 2026 in Manchester. The companion tool, VLLMArchitect, makes the trade-offs explorable; its recommendations are decision support, not a substitute for target-hardware measurement.
Questions about this analysis or want to validate it on target hardware?