MLX is not shipped for this model

The MLX artifacts for this model were removed deliberately. The exporter in export-scripts is kept so the decision can be re-tested against a future ExecuTorch/MLX release, but no .pte or config.json is published here.

Why

Measured on device (iPhone 16, ExecuTorch 1.4.1, Release build, 3 warmup runs + 15 timed runs, median), on lraspp (mobilenet_v3_large):

backend	precision	median latency
Core ML	fp32 (precision matched)	15.8 ms
MLX	fp32 (only precision offered)	75.5 ms
Core ML	fp16 (shipped default)	7.8 ms

MLX is 4.8x slower than Core ML at matched precision, and 9.7x slower than the Core ML build that actually ships.

These exporters only ever declared an MLX fp32 variant, so fp32-vs-fp32 is the fair comparison and it is deliberately the conservative one: MLX still loses it.

Context

MLX was measured behind Core ML on every convolutional vision model tested. The margin varies widely with architecture, so each model carries its own number rather than a blanket figure.

Two things compound the gap. MLX weight quantization cannot shrink a conv model: quantize_model_ only rewrites nn.Linear and embedding modules, and EfficientNet-V2-S holds just 6.0% of its parameters in its single nn.Linear, so 4-bit quantization buys about 5% file size. Core ML by contrast reaches the convolutions.

Scope

This concerns convolution-dominated vision models. It does not generalize:

rfdetr-nano (conv + transformer) measured only 1.24x behind Core ML.
The lfm2.5 text encoders measured 2.7-3.1x faster on MLX than XNNPACK.

MLX suits matmul-heavy graphs. The right backend depends on model class, so this file is not a statement about MLX in general.

Availability history

MLX for this model was only ever published on main / v0.10.0. No earlier tag (v0.9.0 and below) carried an MLX artifact for it, so there is no pinned revision from which these files can still be fetched. They remain recoverable from this repository's git history.