The universal AI bridge for transpiling models and optimizing cross-framework inference.
The universal AI bridge for transpiling models and optimizing cross-framework inference.
Ivy is a high-performance, unified AI framework designed to solve the fragmentation in the machine learning ecosystem. In the 2026 landscape, Ivy serves as the critical 'translation layer' that allows developers to write code in one framework (like PyTorch) and run it on any other backend (JAX, TensorFlow, or PaddlePaddle) with zero overhead. Technically, Ivy achieves this through a graph-to-graph transpilation process and a unified functional API that abstracts framework-specific operations into a common intermediate representation. This architecture enables seamless model migration, cross-backend performance benchmarking, and hardware-agnostic deployment. Beyond its core transpilation engine, the Unify platform integrates an intelligent LLM routing layer, which dynamically selects the most cost-effective or highest-performing model endpoint based on real-time telemetry. As enterprises increasingly adopt multi-cloud and multi-model strategies, Ivy's role as a vendor-neutral infrastructure component positions it as an essential tool for avoiding framework lock-in and optimizing the full lifecycle of neural network development and deployment.
The universal AI bridge for transpiling models and optimizing cross-framework inference.
Quick visual proof for Ivy (by Unify.ai). Helps non-technical users understand the interface faster.
Ivy is a high-performance, unified AI framework designed to solve the fragmentation in the machine learning ecosystem.
Explore all tools that specialize in cross-framework conversion. This domain focus ensures Ivy (by Unify.ai) delivers optimized results for this specific requirement.
Explore all tools that specialize in optimize model inference. This domain focus ensures Ivy (by Unify.ai) delivers optimized results for this specific requirement.
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A source-to-source transpiler that converts code between PyTorch, JAX, and TensorFlow by mapping functional signatures.
A standardized set of over 500+ math and ML operations that execute natively on the active backend.
An orchestration layer that redirects prompts to the optimal LLM based on latency, cost, or quality benchmarks.
Captures operations in a symbolic graph before execution, enabling cross-framework graph optimizations.
A repository of pre-transpiled models ready for immediate deployment in any major framework.
Enables backpropagation through a pipeline that mixes modules from different frameworks.
Automatically profiles transpiled code on specific hardware (NVIDIA, TPU, Apple Silicon) to select the fastest backend.
Install the Ivy core library via pip: pip install ivy-core.
Choose your desired source and target backends (e.g., PyTorch to JAX).
Initialize the Ivy environment with ivy.set_backend('target_backend').
Use ivy.transpile() to convert a source function or module into the target framework.
Validate the transpiled output using the built-in functional testing suite.
Optimize the resulting graph using Ivy’s lazy execution and graph compiler.
Integrate the Unify API key for cloud-based LLM routing features.
Set up performance monitoring to track latency and cost across different backends.
Deploy the optimized model using the Unify Hub for managed hosting.
Configure automated retraining pipelines that remain framework-agnostic.
All Set
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“Highly praised for solving framework fragmentation, though some users note a learning curve for complex custom operations.”
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