| ---
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| license: cc-by-4.0
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| task_categories:
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| - image-classification
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| tags:
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| - benchmark
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| - edge
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| - mobile
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| - latency
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| - quantization
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| pretty_name: TinyEdge edge-inference benchmarks
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| ---
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|
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| # TinyEdge edge-inference benchmarks
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|
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| Independently **measured** latency and accuracy for well-known vision models on
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| real edge devices (phones, tablets — fleet growing), produced by
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| [TinyEdge](https://tinyedge.ai), a device cloud for edge-AI benchmarking.
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| Nothing here is taken from papers or spec sheets: every row is a job executed
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| on the physical device through TinyEdge's production agent, with accuracy
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| measured on a fixed 500-image stratified sample of
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| [ImageNet-V2](https://github.com/modestyachts/ImageNetV2) (matched-frequency)
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| using a standardized preprocessing spec, batch 1, 224x224.
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|
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| ImageNet-V2 top-1 runs systematically below classic ImageNet val for every
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| model (known distribution shift) — treat `accuracy_value` as a *relative*
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| capacity indicator; rankings and deltas are the signal.
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|
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| ## Headline findings (snapshot 2026.06)
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|
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| - **fp16 is a placebo on these NPUs** (onnxruntime-android + NNAPI): identical
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| latency and accuracy to fp32 on every model tested.
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| - **Static int8 (QDQ) is the real lever: 2.6–2.8x faster at bit-identical
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| accuracy** for MobileNetV2 — when the architecture tolerates it.
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| - **Quantization safety is architecture-dependent:** naive post-training int8
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| collapsed MobileNetV3-Small (−57 pts) and EfficientNet-B0 (−32 pts);
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| MobileNetV3-Large lost 4 pts; MobileNetV2 lost nothing.
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| - **Dynamic ("data-free") int8 does not run at all** on Android ONNX Runtime
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| (`ConvInteger` not implemented).
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| - **Published accuracy can invert on-device rankings:** EfficientNet-B2
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| (published +2.9 over B0) measured *no better* than B0 while 51% slower.
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|
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| ## LLM benchmarks (llm-benchmarks.csv)
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|
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| GGUF quantization ladders measured on-device via llama.cpp (CPU): decode and
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| prefill tok/s at the measured-optimal thread count (big cores only — the
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| llama.cpp default of all-cores is 28-39% slower on big.LITTLE SoCs), with
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| WikiText-2 perplexity (fixed 32-chunk subset) as the device-independent
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| quality column. First snapshot: Qwen2.5-0.5B-Instruct, Q8_0→Q3_K_M, on a
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| Snapdragon 662 phone — 14-19 tok/s decode (conversational) at every level;
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| Q6_K dominates Q8_0; prefill (~30-40 tok/s) is the practical bottleneck for
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| long prompts.
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|
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| ## Schema
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| One row = one (model, device, precision/quant) measurement.
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| Key columns: `latency_ms_p50`, `throughput_per_s`, `accuracy_value`
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| (`accuracy_metric` = imagenetv2_top1), `quant_method`, `runtime` (includes the
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| execution provider that actually ran), `sweep` (provenance).
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| The `task` column is `image-classification` for all current rows; LLM rows
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| (decode tok/s, TTFT, RAM) will use the same table with `task=text-generation`.
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|
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| ## Method & provenance
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| Sweep tooling and raw per-run JSON live in the TinyEdge repos; results are
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| snapshot-versioned via the `snapshot` column. Jobs run on battery-policy-aware
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| agents; each result records the exact runtime + execution provider. Eval images
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| are NOT redistributed here — only measurements.
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|
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| Questions / want your model or device measured: https://tinyedge.ai/contact
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|
|
