All variants take and return fp32 tensors — swap the
.pte
file, keep your app code.
precision
file
size (MB)
parity vs fp32 eager (worst corr)
Mac median (ms)*
fp32
dinov2_vits14_xnnpack_fp32.pte
88.4
1.000000
159.0
fp16
dinov2_vits14_xnnpack_fp16.pte
44.8
0.999945
283.7
int8 (dynamic)
dinov2_vits14_xnnpack_int8.pte
24.9
0.998009
155.7
*Mac arm64, single process, median of 10 — a reference point for relative cost
only, not a device number (torch eager fp32 on the same machine: 50.8 ms).
Checked in the task's own units
Correlation is a first filter. These are the numbers that decide:
int8 (dynamic)
— measured in the units that matter for this model — cosine similarity of the embeddings: median 0.9986 over 10 real images, worst 0.9965.
Verification (executorch 1.4.0, torch 2.13.0)
Parity is measured against the fp32 eager model on real image input;
corr
is
the correlation over all elements of each output tensor.
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