LiteRT vs TensorFlow Lite: what changed, plus the old-name new-name cheat sheet

Last verified: 2026-09-05 — LiteRT 2.2.0, LiteRT-LM 0.16.1, litert-torch 0.9.4, ai-edge-litert 2.2.0. Every version and link below was read on that date. On-device tooling moves monthly; check the linked page before you pin anything.

LiteRT is TensorFlow Lite, renamed on 2024-09-04. Same .tflite file format, same models, same Interpreter API. Four things changed since then:

org.tensorflow:tensorflow-lite → com.google.ai.edge.litert:litert. Python tflite-runtime → ai-edge-litert. PyTorch converter ai-edge-torch → litert-torch. CompiledModel. torch.export straight to .tflite. No ONNX step, no TensorFlow graph. TensorFlow Lite packages are in maintenance mode. The tensorflow/lite README says they "only receive critical security and stability updates".

If you start today: com.google.ai.edge.litert:litert:2.2.0 with CompiledModel on Android, ai-edge-litert in Python, litert-torch for PyTorch models, LiteRT-LM for on-device LLMs. An existing TensorFlow Lite app keeps working. You can move one package at a time.

// build.gradle.kts — served from Google Maven, not Maven Central
dependencies { implementation("com.google.ai.edge.litert:litert:2.2.0") }
// keep the asset mmappable:  android { androidResources { noCompress += "tflite" } }
python
import com.google.ai.edge.litert.Accelerator
import com.google.ai.edge.litert.CompiledModel

val model = CompiledModel.create(
    context.assets, "model.tflite",
    CompiledModel.Options(Accelerator.GPU),   // NPU with GPU fallback: Options(Accelerator.NPU, Accelerator.GPU)
    null)                                     // Environment; null = default
val inputs = model.createInputBuffers()
val outputs = model.createOutputBuffers()
inputs[0].writeFloat(inputArray)             // FloatArray in the layout you exported (NCHW from litert-torch)
model.run(inputs, outputs)                   // enqueues on the GPU
val logits = outputs[0].readFloat()          // the readback is what waits
// TensorBuffer and CompiledModel are AutoCloseable; close them or you leak native memory.

Two rules that are easy to miss: with Accelerator.GPU, every op in the graph must be GPU-compatible (there is no CPU fallback inside CompiledModel), and GPU tensors are rank 4 at most.

python3.12 -m venv .venv && . .venv/bin/activate   # ran on 3.12.13 and 3.14.6 on 2026-09-05
pip install ai-edge-litert litert-torch            # pulls torch, jax, transformers; ~2 GB; no TensorFlow
python
import numpy as np, torch, litert_torch
from ai_edge_litert.interpreter import Interpreter

x = torch.randn(1, 3, 224, 224)
litert_torch.convert(model.eval(), (x,)).export("model.tflite")   # torch.export → .tflite, nothing in between

it = Interpreter(model_path="model.tflite"); it.allocate_tensors()
inp, out = it.get_input_details()[0], it.get_output_details()[0]
it.set_tensor(inp["index"], x.numpy()); it.invoke()
print(it.get_tensor(out["index"]))

from ai_edge_litert.compiled_model import CompiledModel
from ai_edge_litert.hardware_accelerator import HardwareAccelerator
cm = CompiledModel.from_file("model.tflite", HardwareAccelerator.CPU)
ins, outs = cm.create_input_buffers(0), cm.create_output_buffers(0)
ins[0].write(x.numpy()); cm.run_by_index(0, ins, outs)
print(outs[0].read(10, np.float32))

Run on 2026-09-05 with a conv + nn.MultiheadAttention model: conversion took 1–2 s, outputs were within 7e-7 of PyTorch, identical results on Python 3.12 and 3.14.

Use it when you deploy .tflite models on Android with GPU or NPU, when you want a torch.export-based converter, or when you need a Google-maintained LLM runtime with NPU backends.

Do not pick it for an iOS-only app today. The iOS path is still the TensorFlow Lite pod, and Core ML, MLX and llama.cpp have more direct iOS routes. For arbitrary Hugging Face LLMs on a Mac, GGUF with llama.cpp or MLX needs no conversion step; LiteRT-LM needs a .litertlm bundle (Hugging Face litert-community has 343 models on 2026-09-05, many ungated, Qwen2.5-1.5B-Instruct among them).

ExecuTorch (PyTorch's own runtime, torch.export → .pte), ONNX Runtime Mobile (Maven AAR, NNAPI / XNNPACK / QNN providers), llama.cpp (GGUF, Metal / Vulkan / CPU), MLX (Apple Silicon). All four are reasonable defaults. This page is about what LiteRT calls things, not a ranking.

pip install tflite-runtime. litert:2.1.0 next to a table that lists 2.2.0 as latest./edge/litert/next/*). Blogs say LiteRT; the version-bearing statement is the GitHub v2.1.0 note ("beta… officially recommending that developers begin their transition").mediapipe/util/tflite/operations/) and issues..task and .litertlm. Is TensorFlow Lite deprecated? Maintenance mode: "only receive critical security and stability updates. All active on-device ML development… transitioned to LiteRT" (tensorflow/lite README).

Do my .tflite files still work? Yes. Format and extension are unchanged.

Does LiteRT run on iOS? Through the TensorFlow Lite pod for now. LiteRT-LM has a Swift package in early preview.

Where are ready-made models? Hugging Face litert-community (343 models, .tflite and .litertlm) and Kaggle Models (the filter is still named tfLite).