Hardware specialization vs. cross-platform backbone trade-off

Determine whether it is preferable, for on-device deployment of the Liquid Foundation Models (LFM2), to specialize model variants and quantization schemes to specific hardware targets (e.g., particular CPUs, NPUs, or GPUs) or to maintain a single cross-platform backbone, by characterizing the impact of each approach on time-to-first-token, decode latency, prefill throughput, peak memory, and downstream quality under edge constraints.

Background

The LFM2 architecture and hardware-in-the-loop search were tuned primarily for batch size 1, low-latency CPU and mobile SoC deployments using specific quantization settings, with accelerators not central to the search. While LFM2 runs competitively on modern NPUs and GPUs, the authors do not claim the resulting designs are optimal for accelerator-rich or large-batch server settings.

Given evolving edge runtimes, compiler stacks, and kernel libraries, the authors highlight an unresolved decision: whether to specialize model variants per hardware target or maintain a single cross-platform backbone. Resolving this trade-off requires careful measurement of efficiency and quality across diverse devices and deployment configurations.