litert-torch
Support PyTorch model conversion with LiteRT.
Decision gist · record as of 2026-08-14
Yes, if you are converting PyTorch models for on-device deployment and targeting Android, iOS, or IoT. The library is actively maintained, has low install friction, and offers both general and LLM-specific conversion paths. Conditions: requires Python 3.10 through 3.13 (3.11 recommended), Linux, and a large dependency footprint; the Generative API is Alpha, so production LLM workflows should expect iteration.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Python >=3.10 and <3.14 required; Python 3.11 highly recommended.
- Linux only.
- Requires PyTorch >=2.4.0 and tf-nightly.
License · maintenance · safety
permissive license (permissive) — Permissive license allows commercial and private use with minimal restrictions.
last release 2026-08-04 (10 days) · last repo commit 2026-08-14 · 1,079 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 166,622 downloads/mo, #10,487 on PyPI
Alternatives
Verify before relying
pip install litert-torch
import torch
import litert_torch
model = torch.nn.Linear(10, 2)
with torch.no_grad():
sample_inputs = (torch.randn(1, 10),)
edge_model = litert_torch.convert(model.eval(), sample_inputs)
edge_model.export("model.tflite")- Specific coverage of Core ATen operators and which PyTorch operations are unsupported in conversion
- Performance benchmarks or latency comparisons for converted models on target devices
- Quantization support details beyond the mention of Generative API quantization
- Whether GPU/NPU support is production-ready or experimental
What it is and what it does
LiteRT Torch bridges PyTorch and on-device deployment by converting trained PyTorch models into LiteRT's .tflite format. The library integrates with torch.export() to capture models, then serializes them for execution on mobile and IoT hardware. It provides two main workflows: a PyTorch Converter (Beta) for general model conversion, and a Generative API (Alpha) specialized for transformer-based models and large language models with quantization support.
The package targets developers building edge ML applications where models must run entirely on-device without cloud connectivity. It handles the conversion pipeline and offers initial support for CPU, GPU, and NPU execution. With 19 runtime dependencies including torch, transformers, jax, and tf-nightly, it brings a substantial environment; deployment of the converted .tflite files themselves requires the separate LiteRT runtime.
Use it for
- Convert a trained vision model to .tflite for inference on Android or iOS devices
- Quantize and optimize a transformer-based LLM for on-device deployment using the Generative API
- Export a PyTorch model for IoT edge devices that cannot reach cloud inference services
- Package a converted model and tokenizer into a .litertlm container for production mobile deployment
- Benchmark model performance on target hardware using LiteRT's compiled model API after conversion
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are converting PyTorch models for on-device deployment and targeting Android, iOS, or IoT.
The library is actively maintained, has low install friction, and offers both general and LLM-specific conversion paths. Conditions: requires Python 3.10 through 3.13 (3.11 recommended), Linux, and a large dependency footprint; the Generative API is Alpha, so production LLM workflows should expect iteration.
Install
litert-torch on PyPI
Before you install
Low friction; pure Python wheel. Active maintenance with release 10 days old and recent commits. However, 19 runtime dependencies including torch, transformers, jax, and tf-nightly create a substantial environment footprint.
Python >=3.10 and <3.14 required; Python 3.11 highly recommended. Linux only. Requires PyTorch >=2.4.0 and tf-nightly.
License in practice
Permissive license allows commercial and private use with minimal restrictions.
Quickstart
pip install litert-torch
import torch
import litert_torch
model = torch.nn.Linear(10, 2)
with torch.no_grad():
sample_inputs = (torch.randn(1, 10),)
edge_model = litert_torch.convert(model.eval(), sample_inputs)
edge_model.export("model.tflite")Verify before relying
- Specific coverage of Core ATen operators and which PyTorch operations are unsupported in conversion
- Performance benchmarks or latency comparisons for converted models on target devices
- Quantization support details beyond the mention of Generative API quantization
- Whether GPU/NPU support is production-ready or experimental
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 19 packagesabsl-pynumpyscipysafetensorsmultipledispatchtransformerskagglehubtabulatetorchai-edge-litertai-edge-quantizerlitert-convertertorchaojaxjaxtypingfiresentencepiecerichlitert-lm-builder |
| Maintenance | Actively maintained 10 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 166,622 / month, #10,487 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: DevelopersIntended Audience :: EducationIntended Audience :: Science/ResearchLicense :: OSI Approved :: Apache Software LicenseProgramming Language :: Python :: 3Programming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Topic :: Scientific/EngineeringTopic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Scientific/Engineering :: MathematicsTopic :: Software DevelopmentTopic :: Software Development :: LibrariesTopic :: Software Development :: Libraries :: Python Modules |
Evidence: litert_torch-0.9.3-py3-none-any.whl
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See also ai-edge-litert · onnx2tf · litert-converter · litert-lm-builder · ai-edge-litert-nightly · tflite-runtime · pnnx · ai-edge-quantizer · executorch · pte-adapter-model-explorer