Datasets:

TinyEdge
/

edge-inference-benchmarks

license: cc-by-4.0
task_categories:
  - image-classification
tags:
  - benchmark
  - edge
  - mobile
  - latency
  - quantization
pretty_name: TinyEdge edge-inference benchmarks

TinyEdge edge-inference benchmarks

Independently measured latency and accuracy for well-known vision models on real edge devices (phones, tablets — fleet growing), produced by TinyEdge, a device cloud for edge-AI benchmarking. Nothing here is taken from papers or spec sheets: every row is a job executed on the physical device through TinyEdge's production agent, with accuracy measured on a fixed 500-image stratified sample of ImageNet-V2 (matched-frequency) using a standardized preprocessing spec, batch 1, 224x224.

ImageNet-V2 top-1 runs systematically below classic ImageNet val for every model (known distribution shift) — treat accuracy_value as a relative capacity indicator; rankings and deltas are the signal.

Headline findings (snapshot 2026.06)

fp16 is a placebo on these NPUs (onnxruntime-android + NNAPI): identical latency and accuracy to fp32 on every model tested.
Static int8 (QDQ) is the real lever: 2.6–2.8x faster at bit-identical accuracy for MobileNetV2 — when the architecture tolerates it.
Quantization safety is architecture-dependent: naive post-training int8 collapsed MobileNetV3-Small (−57 pts) and EfficientNet-B0 (−32 pts); MobileNetV3-Large lost 4 pts; MobileNetV2 lost nothing.
Dynamic ("data-free") int8 does not run at all on Android ONNX Runtime (ConvInteger not implemented).
Published accuracy can invert on-device rankings: EfficientNet-B2 (published +2.9 over B0) measured no better than B0 while 51% slower.

LLM benchmarks (llm-benchmarks.csv)

GGUF quantization ladders measured on-device via llama.cpp (CPU): decode and prefill tok/s at the measured-optimal thread count (big cores only — the llama.cpp default of all-cores is 28-39% slower on big.LITTLE SoCs), with WikiText-2 perplexity (fixed 32-chunk subset) as the device-independent quality column. First snapshot: Qwen2.5-0.5B-Instruct, Q8_0→Q3_K_M, on a Snapdragon 662 phone — 14-19 tok/s decode (conversational) at every level; Q6_K dominates Q8_0; prefill (~30-40 tok/s) is the practical bottleneck for long prompts.

Schema

One row = one (model, device, precision/quant) measurement. Key columns: latency_ms_p50, throughput_per_s, accuracy_value (accuracy_metric = imagenetv2_top1), quant_method, runtime (includes the execution provider that actually ran), sweep (provenance). The task column is image-classification for all current rows; LLM rows (decode tok/s, TTFT, RAM) will use the same table with task=text-generation.

Method & provenance

Sweep tooling and raw per-run JSON live in the TinyEdge repos; results are snapshot-versioned via the snapshot column. Jobs run on battery-policy-aware agents; each result records the exact runtime + execution provider. Eval images are NOT redistributed here — only measurements.

Questions / want your model or device measured: https://tinyedge.ai/contact