TFLite LiteRT TensorFlow Test vs 闪电口袋

How fast does your .tflite model actually run on this iPhone? Pick CPU, GPU or Neural Engine. Get real latency and QPS. No Mac, no Xcode project, no Bazel build, no cloud, no account. Open a .tflite file from the Files app, pick an accelerator, run it. A real measurement on the exact device you care about, in about a minute — no Bazel workspace, no benchmark tool built from source. PICK THE ACCELERATOR - Neural Engine — attaches the Core ML delegate, so supported operators run on the Apple Neural Engine - GPU — attaches the GPU delegate, backed by Metal - CPU — the plain interpreter, with a thread count you choose: 1, 2, 4, 6 or 8 One configuration per run: run it, change the delegate or thread count, load it again, compare the numbers yourself. WHAT YOU GET - Latency: mean, min and max, in milliseconds - Queries per second - Total queries completed and total run duration - A query count you set per run, from 50 to 5,000 invocations Mean, min and max time the model inference call only — no pre-processing, no post-processing. Duration covers the whole loop. The screen stays awake during a run. WHAT YOU SEE ABOUT THE MODEL - Every input and output tensor: index, name, shape and data type (uInt8, int32, float16, float32 and the rest) - Model file size and framework - Live app memory usage against the memory available to the app, refreshed while the model runs - A device tab with model identifier, system version, disk space, and the exact TensorFlow Lite runtime version this build links against A MobileNet model ships in the app and loads on launch, so you see a real measurement before importing your own. READ THIS BEFORE YOU TRUST A NUMBER Inference runs on a zeroed dummy input. The app fills every input tensor with zeros and invokes the model, so what you measure is the compute cost of the graph on the accelerator you picked — latency and throughput, nothing else. It is not an accuracy test. It will not tell you whether your model gives the correct answer, and it does not show output tensor values. Ask it "how fast", not "how correct". What it does not do, stated up front: no side-by-side accelerator comparison in one run, no per-layer or per-operator profiling, no result export, no image or real-data input. It opens .tflite files only. PRIVACY Your model never leaves the device. Loading, inspection, inference and timing all happen locally on your iPhone or iPad. No account, no sign-in — your models and your results are never uploaded. TensorFlow Lite is now called LiteRT; the format and the .tflite extension are unchanged. TensorFlow, the TensorFlow logo and any related marks are trademarks of Google Inc. Not affiliated with or endorsed by Google.

【闪电口袋】是你随身携带的 HTML 收藏夹。 把日常使用Ai 工具生成的 HTML 笔记、demo 和代码片段、动效灵感统统装进口袋，随时随地收藏、搜索、分享。 随手收藏 • 从剪贴板一键保存网页内容 • 支持 ZIP 包导入完整 HTML 项目 • iCloud Drive 自动导入，跨设备同步不丢失 秒速找回 • 语义搜索：输入"动画""按钮特效"等大白话就能找到，不用死记文件名 • AI 智能分类：自动识别内容，给出分类建议，一键采纳或忽略 • 缩略图预览：每张卡片一眼看到作品效果，不用点开就知道是什么 轻松分享 • 生成带品牌标识的效果卡片，分享到微信、朋友圈更有辨识度 • 分享图片自带下载二维码，朋友扫码即可下载 App • 所见即所得，分享前可双指缩放、单指拖动微调画面 隐私安全 • 智能分类在设备本地完成，文件不离开手机 • 无需注册，无广告，完全免费 无论你是产品人使用 Ai 制作的 HTML demo，还是学生使用 Ai 生成的笔记，以及设计师收藏动效参考、教师整理教学课件，还是前端开发者管理代码片段，闪电口袋都是你的得力助手。 立即下载，现在就把好作品装进口袋，随时收藏、秒速找回、轻松分享！