Related Products
|
||||||
About
ExecuTorch is PyTorch’s open source framework for deploying AI/ML models directly to edge devices, enabling text, vision, speech, recommendation, and multimodal inference without requiring the cloud. It exports models from PyTorch without intermediate conversion formats, preserves ATen operators, and uses ahead-of-time compilation to optimize performance for target hardware before deployment. Its modular design lets developers choose compile-time and runtime optimizations while staying inside the familiar PyTorch ecosystem, including torchao for quantization. A portable C++ runtime with a base footprint of about 50 KB can run on smartphones, desktops, embedded systems, microcontrollers, DSPs, and Cortex-M processors. ExecuTorch supports Android, iOS, Linux, Windows, macOS, and WebAssembly, with native APIs for C++, Swift, Kotlin, and Objective-C.
|
About
Luminal is a machine-learning framework built for speed, simplicity, and composability, focusing on static graphs and compiler-based optimization to deliver high performance even for complex neural networks. It compiles models into minimal “primops” (only 12 primitive operations) and then applies compiler passes to replace those with device-specific optimized kernels, enabling efficient execution on GPU or other backends. It supports modules (building blocks of networks with a standard forward API) and the GraphTensor interface (typed tensors and graphs at compile time) for model definition and execution. Luminal’s core remains intentionally small and hackable, with extensibility via external compilers for datatypes, devices, training, quantization, and more. Quick-start guidance shows how to clone the repo, build a “Hello World” example, or run a larger model like LLaMA 3 using GPU features.
|
|||||
Platforms Supported
Windows
Supported
Mac
Supported
Linux
Supported
Cloud
Not Supported
On-Premises
Supported
iPhone
Supported
iPad
Supported
Android
Supported
Chromebook
Not Supported
|
Platforms Supported
Windows
Not Supported
Mac
Not Supported
Linux
Not Supported
Cloud
Supported
On-Premises
Supported
iPhone
Not Supported
iPad
Not Supported
Android
Not Supported
Chromebook
Not Supported
|
|||||
Audience
AI developers and engineering teams that need to deploy optimized PyTorch models for on-device inference across smartphones, embedded systems, and microcontrollers
|
Audience
ML infrastructure engineers and researchers seeking a tool offering a deployment framework for GPUs or heterogeneous devices
|
|||||
Support
Phone Support
Not Supported
24/7 Live Support
Not Supported
Online
Supported
|
Support
Phone Support
Not Supported
24/7 Live Support
Not Supported
Online
Supported
|
|||||
API
Offers API
Supported
|
API
Offers API
Supported
|
|||||
Screenshots and Videos |
Screenshots and Videos |
|||||
Pricing
Free
Free Version
Supported
Free Trial
Not Supported
|
Pricing
No information available.
Free Version
Not Supported
Free Trial
Not Supported
|
|||||
Reviews/
|
Reviews/
|
|||||
Training
Documentation
Supported
Webinars
Not Supported
Live Online
Not Supported
In Person
Not Supported
|
Training
Documentation
Supported
Webinars
Not Supported
Live Online
Supported
In Person
Not Supported
|
|||||
Company InformationExecuTorch
United States
executorch.ai/
|
Company InformationLuminal
United States
luminalai.com
|
|||||
Alternatives |
Alternatives |
|||||
|
|
||||||
|
|
|
|||||
|
|
||||||
|
|
|
|||||
Categories |
Categories |
|||||
Integrations
C++
Supported
Facebook
Supported
Hugging Face
Not Supported
Instagram
Supported
Kotlin
Supported
LLaVA
Supported
Llama 3
Not Supported
Llama 3.2
Supported
Muse Glimmer
Supported
Objective-C
Supported
|
Integrations
C++
Not Supported
Facebook
Not Supported
Hugging Face
Supported
Instagram
Not Supported
Kotlin
Not Supported
LLaVA
Not Supported
Llama 3
Supported
Llama 3.2
Not Supported
Muse Glimmer
Not Supported
Objective-C
Not Supported
|
|||||
|
|
|