NVIDIA AITune

NVIDIA AITune is an inference toolkit designed for tuning and deploying Deep Learning models with a focus on NVIDIA GPUs. It provides model tuning capabilities through compilation and conversion paths that can significantly improve inference speed and efficiency across various AI workloads including Computer Vision, Natural Language Processing, Speech Recognition, and Generative AI.

The toolkit enables seamless tuning of PyTorch models and pipelines using various backends such as TensorRT, Torch-TensorRT, TorchAO, Torch Inductor, and ONNX Runtime through a single Python API. The resulting tuned models are ready for deployment in production environments.

NVIDIA AITune works with your environment — relying first on your software versions — and selects the best-performing backend for your software and hardware setup, guiding you to supported technologies.

Note: This is the first release. The API may change in future versions.

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Features at Glance

The distinct capabilities of NVIDIA AITune are summarized in the feature matrix:

Feature	Description
Ease-of-use	Single line of code to run all possible tuning paths directly from your source code
Wide Backend Support	Compatible with various tuning backends including TensorRT, Torch-TensorRT, TorchAO, Torch Inductor, and ONNX Runtime
Model Tuning	Enhance the performance of models such as ResNET and BERT for efficient inference deployment
Pipeline Tuning	Streamline Python code pipelines for models such as Stable Diffusion and Flux using seamless model wrapping and tuning
Model Export and Conversion	Automate the process of exporting and converting models between various formats with focus on TensorRT, Torch-TensorRT, and ONNX Runtime
Correctness Testing	Ensures tuned models produce correct outputs by validating on provided data samples
Performance Profiling	Profiles models to select the optimal backend based on performance metrics such as latency and throughput
Model Persistence	Save and load tuned models for production deployment with flexible storage options
JIT tuning	Just-in-time tuning of a model or a pipeline without any code changes required

When to Use AITune

AITune provides compute graph optimizations for PyTorch models at the nn.Module level. Use AITune when you want automated inference optimization with minimal code changes.

If your model is supported by a dedicated serving framework and benefits from runtime optimizations (e.g. continuous batching, speculative decoding), use frameworks like TensorRT-LLM, vLLM, or SGLang for best performance. Use AITune for general PyTorch models and pipelines that lack such specialized tooling.

Prerequisites

Before proceeding with the installation of NVIDIA AITune, ensure your system meets the following criteria:

• Operating System: Linux (Ubuntu 22.04+ recommended)
• Python: Version 3.10 or newer
• PyTorch: Version 2.7 or newer
• TensorRT: Version 10.5.0 or higher (for TensorRT backend)
• NVIDIA GPU: Required for GPU-accelerated tuning

You can use NGC Containers for PyTorch which contain all necessary dependencies:

• PyTorch NGC Container

Install

NVIDIA AITune can be installed from pypi.org.

Installing from PyPI (Recommended)

pip install --extra-index-url https://pypi.nvidia.com aitune

Installing from Source

# Clone the repository
git clone https://github.com/ai-dynamo/aitune
cd aitune
pip install --extra-index-url https://pypi.nvidia.com .

or use editable mode for development:

pip install --extra-index-url https://pypi.nvidia.com -e .

Quick Start

This quick start provides examples of tuning and deployment paths available in NVIDIA AITune.

NVIDIA AITune enables seamless tuning of models for deployment (for example, converting them to TensorRT) without requiring changes to your original Python pipelines.

NVIDIA AITune supports two modes:

• Ahead-of-time tuning — provide a model or a pipeline, and a dataset/dataloader. You can either rely on inspect to detect promising modules to tune or manually select them.
• Just-in-time tuning — set a special environment variable, run your script without changes, and AITune will, on the fly, detect modules and tune them one by one.

Ahead-of-time mode is more powerful and allows you to tweak more settings, whereas just-in-time works out of the box but offers less control over the tuning process. For a more detailed comparison, see the Comparison between AOT and JIT tuning section.

Enabling logging

The tuning process guides the user through decisions and steps that are performed to tune every selected module.

We recommend to enable the INFO logging level for better verbosity.

import logging

logging.basicConfig(level=logging.INFO, force=True)

Ahead-of-time tuning

The code below demonstrates Stable Diffusion pipeline tuning.

You can annotate torch.nn.Modules manually or use the inspect functionality to have modules picked automatically; you can then verify them and schedule them for tuning.

First, install the required third-party dependencies:

pip install transformers diffusers torch

Then initialize the pipeline:

# HuggingFace dependencies
import torch
from diffusers import DiffusionPipeline

# Import AITune
import aitune.torch as ait

# Initialize pipeline
pipe = DiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16)
pipe.to("cuda")

Next, inspect the pipeline components and display the summary:

# Prepare input data
input_data = [{"prompt": "A beautiful landscape with mountains and a lake"}]

# Inspect pipeline to get modules
modules_info = ait.inspect(pipe, input_data)


# Optional: inference function, if you need more control over execution
def infer(prompt):
    return pipe(prompt, width=1024, height=1024, num_inference_steps=10)


# modules_info = ait.inspect(pipe, input_data, inference_function=infer)

# Display modules info
modules_info.describe()

Finally, wrap the selected modules and tune within the pipeline:

# Wrap modules for tuning
modules = modules_info.get_modules()
pipe = ait.wrap(pipe, modules)

# Tune pipeline
ait.tune(pipe, input_data)

At this point, you can use the pipeline to generate predictions with the tuned models directly in Python:

# Run inference on tuned pipeline
images = pipe(["A beautiful landscape with mountains and a lake"])
image = images[0][0]

# Save image for preview
image.save("landscape.png")

Once the pipeline has been tuned, you can save the best-performing version of the modules for later deployment:

ait.save(pipe, "tuned_pipe.ait")

And load the tuned pipeline directly:

pipe = DiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16)
pipe.to("cuda")
ait.load(pipe, "tuned_pipe.ait")

Just-in-time tuning

In this mode, there is no need to modify the user's code. At the beginning, AITune uses a few inferences to detect model architecture and hierarchy of a model. Then it tries to tune modules one by one starting from the top. If there is one of the following conditions:

• a graph break is detected, i.e., torch.nn.Module contains conditional logic on inputs, meaning there is no guarantee of a static, correct graph of computations, or
• there is an error during tuning

that module is left unchanged and AITune tries to tune its children. This process continues until the module depth reaches a configured limit.

First, install the required third-party dependencies:

pip install transformers diffusers torch

Prepare the example script for tuning my_script.py:

# Enable JIT tuning
import aitune.torch.jit.enable

# HuggingFace dependencies
import torch
from diffusers import DiffusionPipeline

# Initialize pipeline
pipe = DiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16)
pipe.to("cuda")

# First call - tuning the model
pipe("A beautiful landscape with mountains and a lake")

# Second call - using tuned model
pipe("A beautiful landscape with mountains and a lake")

You can then run your script:

python my_script.py

Note: The import aitune.torch.jit.enable must be a first import in your code. The alternative option is to use export AUTOWRAPT_BOOTSTRAP=aitune_enable_jit_tuning to avoid any source code modification.

Configuring just-in-time tuning

If there is a need to adjust just-in-time options, you can do it but currently this requires modifying code to import the JIT config:

from aitune.torch.jit.config import config
from aitune.torch.backend import TensorRTBackend
from aitune.torch.tune_strategy import FirstWinsStrategy

config.max_depth_level = 1  # change the default maximum depth level for nested modules to be tuned
config.detect_graph_breaks = False  # turn off graph break detection
config.strategy = FirstWinsStrategy(backends=[TensorRTBackend()])  # change the tune strategy

Comparison between ahead-of-time and just-in-time tuning

The ahead-of-time tuning gives you the most control over the tuning process:

• it detects the batch axis and dynamic axes (axes that change shape independently of batch size, e.g., sequence length in LLMs)
• allows picking modules to tune
• you can pick a tuning strategy (e.g., best throughput) for the whole process or per-module
• you can pick tuning backends (e.g., TensorRT, TorchInductor, TorchAO, ONNXRuntime) which will be used by the strategy
• you can mix different backends in the same model/pipeline
• you can manually verify the tuning process (note: AITune performs basic checks for NaNs and errors)
• you can save the resulting artifact and later read it from disk

The big advantage of just-in-time tuning is that you don't need to modify the user's script to tune a model. However, it has some disadvantages - since it cannot access data directly (you don't provide a dataloader):

• it cannot deduce batch size nor do benchmarking
• input/output shapes depend on the data seen, so for example, TRT backend will build a profile only for that data
• it needs at least two inference calls - first to get model/pipeline hierarchy and second one for actual tuning
• if you need dynamic axes (e.g., TRT backend), you need to provide two different batch sizes
• there is limited support of strategies due to unknown batch size
• you can specify backends for the whole model

The following table summarizes the difference between modes:

Feature	Ahead-of-time	Just-in-time
Detecting dynamic axes	Yes	Yes
Extrapolating batches	Yes	No
Benchmarking	Yes	No (no extrapolating batches)
Modules for tuning	User has full control	Picked automatically
Selecting tune strategy	Global or per module	Global
Available strategies	All	Limited (no benchmarking)
Tune time	Slow	Quick
Saving artifacts	Yes	No
Load tuned model time	Quick	Re-tuning required
Code changes required	Yes	No
Caching	Yes	No

Note: Currently, JIT mode does not support caching results, i.e., every time a new Python interpreter starts, the tuning process starts from scratch.

Core Functionalities

Inspect for AOT tuning

The inspect function allows you to analyze PyTorch models and pipelines to understand their structure, parameters, and execution flow. It provides detailed insights into model architecture and helps identify tuning opportunities.

import aitune.torch as ait
import torch.nn as nn


class SimpleModel(nn.Module):
    def __init__(self):
        super().__init__()
        self.linear = nn.Linear(100, 10)

    def forward(self, x):
        return self.linear(x)


model = SimpleModel()

# Inspect the model
ait.inspect(model, dataset)

Inspect for JIT tuning

JIT tuning also has a corresponding inspect mode which gathers information about the model/pipeline and allows checking model input and output arguments, hierarchy of the model, etc.

Here is a short snippet how to use it:

# required imports
import aitune.torch.jit.enable_inspection as inspection

# your code goes here
# ...

# you can export report to html file
inspection.save_report("filename.html", "YOUR_MODEL_NAME")

Tune

The tune function is the core functionality that automatically tunes your PyTorch models and pipelines for optimal inference performance. It supports various backends and automatically selects the best performing configuration.

import aitune.torch as ait
import torch

# Define your model
model = SimpleModel()

# Wrap the model
model = ait.Module(model)


# Define inference function
def inference_fn(x):
    return model(x)


# Tune the model
ait.tune(
    func=inference_fn,
    dataset=torch.randn(1, 100),
)

Save

The save function allows you to persist tuned models for later use. It stores tuned and original module weights together in a single file with a .ait extension. Apart from the checkpoint file, there is also a SHA hash file.

# Save the tuned model
import aitune.torch as ait

ait.save(model, "tuned_model.ait")

Example output:

checkpoints/
├── tuned_model
├── tuned_model.ait
└── tuned_model_sha256_sums.txt

You can copy the checkpoint file tuned_model.ait and SHA sums file to a target host or folder to use it for inference.

Note: We recommend to deploy *.ait package on the same hardware as tuning has been performed for functional and performance compatibility.

Load

The load function enables you to load previously tuned models from a checkpoint file.

# Load the tuned model
import aitune.torch as ait

tuned_model = ait.load(model, "tuned_model.ait")

On first load, the checkpoint file is decompressed and the tuned and original module weights are loaded. Subsequent loads will use the decompressed weights from the same folder.

Backends

NVIDIA AITune supports multiple tuning backends, each with different characteristics and use cases. The backends align with a common interface for the build and inference process.

TensorRT Backend

The TensorRT backend provides highly optimized inference using NVIDIA's TensorRT engine. It offers the best performance for production deployments. The backend integrates TensorRT Model Optimizer in a seamless flow.

from aitune.torch.backend import TensorRTBackend, TensorRTBackendConfig, ONNXAutoCastConfig

config = TensorRTBackendConfig(quantization_config=ONNXAutoCastConfig())  # FP16 autocast through ModelOpt
backend = TensorRTBackend(config)

CUDA Graphs Support

The TensorRT backend supports CUDA Graphs for reduced CPU overhead and improved inference performance. CUDA Graphs automatically capture and replay GPU operations, eliminating kernel launch overhead for repeated inference calls. This feature is disabled by default.

Keep in mind that graphs are automatically recaptured when input shapes change.

from aitune.torch.backend import TensorRTBackend, TensorRTBackendConfig

# Enable CUDA Graphs for optimized inference
config = TensorRTBackendConfig(use_cuda_graphs=True)
backend = TensorRTBackend(config)

Torch-TensorRT Backend (JIT)

Torch-TensorRT JIT backend integrates TensorRT tuning directly into PyTorch, providing seamless tuning without model conversion through torch.compile.

import torch
from aitune.torch.backend import TorchTensorRTJitBackend, TorchTensorRTJitBackendConfig, TorchTensorRTConfig

config = TorchTensorRTJitBackendConfig(compile_config=TorchTensorRTConfig(enabled_precisions={torch.float16}))
backend = TorchTensorRTJitBackend(config)

Torch-TensorRT Backend (AOT)

Torch-TensorRT backend integrates TensorRT tuning directly into PyTorch, providing seamless tuning without model conversion through torch_tensorrt.compile.

import torch
from aitune.torch.backend import TorchTensorRTAotBackend, TorchTensorRTAotBackendConfig, TorchTensorRTConfig

config = TorchTensorRTAotBackendConfig(compile_config=TorchTensorRTConfig(enabled_precisions={torch.float16}))
backend = TorchTensorRTAotBackend(config)

TorchAO Backend

TorchAO backend leverages PyTorch's AO (Accelerated Optimization) framework for model tuning.

from aitune.torch.backend import TorchAOBackend

backend = TorchAOBackend()

Torch Inductor Backend (JIT)

Torch Inductor JIT backend uses PyTorch's Inductor compiler through torch.compile for model tuning.

from aitune.torch.backend import TorchInductorJitBackend

backend = TorchInductorJitBackend()

Torch Inductor Backend (AOT)

Torch Inductor AOT backend uses PyTorch's AOT Inductor compiler to produce a compiled artifact that can be saved and loaded with AITune checkpoints.

from aitune.torch.backend import TorchInductorAotBackend

backend = TorchInductorAotBackend()

ONNXRuntime Backend

ONNXRuntime backend exports the selected PyTorch module to ONNX and runs inference through ONNX Runtime with CUDA or TensorRT execution providers.

from aitune.torch.backend import ONNXRuntimeBackend, ONNXRuntimeBackendConfig, ONNXExecutionProvider

config = ONNXRuntimeBackendConfig(execution_provider=ONNXExecutionProvider.CUDA)
backend = ONNXRuntimeBackend(config)

Tune Strategies

NVIDIA AITune provides different strategies for selecting the optimal backend configuration. The strategies align with a common interface for the tuning process.

Not every backend can tune every model — each relies on different compilation technology with its own limitations (e.g., ONNX export for TensorRT, graph breaks in Torch Inductor, unsupported layers in TorchAO). Strategies control how AITune handles this.

FirstWinsStrategy

Tries backends in priority order and returns the first one that builds, validates correctness, and beats the Torch eager baseline by the configured threshold. If a backend fails or is slower than baseline, the strategy moves on to the next candidate instead of aborting.

from aitune.torch.tune_strategy import FirstWinsStrategy

strategy = FirstWinsStrategy(backends=[TensorRTBackend(), TorchInductorJitBackend()])

OneBackendStrategy

Uses exactly one backend, failing immediately with the original error if it cannot build. Use this when you have already validated that a backend works and want deterministic behavior. Unlike FirstWinsStrategy with a single backend, OneBackendStrategy surfaces the original exception rather than catching it.

from aitune.torch.tune_strategy import OneBackendStrategy

strategy = OneBackendStrategy(backend=TensorRTBackend())

MaxThroughputStrategy

Profiles all compatible backends and selects the fastest one that beats the Torch eager baseline, falling back to eager when no user backend is faster. Use this when maximum throughput matters and you can afford longer tuning time.

from aitune.torch.tune_strategy import MaxThroughputStrategy

strategy = MaxThroughputStrategy(backends=[TensorRTBackend(), TorchInductorJitBackend(), TorchEagerBackend()])

Profiling with NVTX

NVIDIA AITune includes NVTX (NVIDIA Tools Extension) annotations for profiling and debugging. NVTX marks key operations in the code, making them visible in profiling tools like NVIDIA Nsight Systems.

Note: NVTX annotations are disabled by default to avoid overhead in production environments.

Enabling NVTX

To enable NVTX profiling, set the environment variable before running your script:

export AITUNE_NVTX_EVENTS=1
python your_script.py

Using with Nsight Systems

Once enabled, you can profile your application with Nsight Systems:

AITUNE_NVTX_EVENTS=1 nsys profile -o output.nsys-rep -trace=cuda,nvtx,osrt python your_script.py

The NVTX annotations will appear as colored regions in the timeline, helping you identify:

• Backend inference calls (TensorRT, Torch-TensorRT, TorchAO, etc.)
• Tuning operation
• Performance bottlenecks

Hardware Metrics

NVIDIA AITune can collect hardware metrics during tuning and inference, giving you visibility into resource utilization per module and backend. Metrics are collected in a background process and reported at program exit.

Note: Hardware metrics collection is disabled by default to avoid overhead in production environments.

Enabling Hardware Metrics

Set the environment variable before running your script:

export AITUNE_HARDWARE_METRICS=1
python your_script.py

Collected Metrics

The following metrics are sampled continuously (every 100 ms by default) and aggregated per module and backend:

Category	Metrics
GPU memory (per device)	cuda:N used memory [GB]
GPU utilization (per device)	cuda:N utilization mean / max [%]
GPU power (per device)	cuda:N power mean / max [W]
Host CPU	CPU utilization [%]
Host memory	Used / free system memory
PyTorch allocator	Allocated and reserved CUDA memory

GPU metrics require NVML (available when running on a system with NVIDIA drivers). If NVML is unavailable, only host and PyTorch metrics are collected.

Output

At program exit, AITune logs a summary table and writes a CSV file to the working directory.

By default a timestamped filename is used:

hardware_metrics_20260402_153012.csv

To write to a fixed path instead, set AITUNE_HARDWARE_METRICS_PATH:

export AITUNE_HARDWARE_METRICS_PATH=hardware_metrics.csv

The log summary looks like:

INFO Hardware metrics summary:
╒════════════════════════╤══════════════════════════════╤════════════╤════════════╤══════════════╤═════════════╤═════════════╤═════════════╕
│ Module                 │ Backend                      │    Host    │   Cuda:0   │    Cuda:0    │   Cuda:0    │  Power [W]  │  Power [W]  │
│                        │                              │  Mem [GB]  │  Mem [GB]  │  Util% mean  │  Util% max  │    mean     │     max     │
╞════════════════════════╪══════════════════════════════╪════════════╪════════════╪══════════════╪═════════════╪═════════════╪═════════════╡
│ CLIPTextModel          │ TensorRTBackend(             │   15.53    │    1.73    │     1.03     │      7      │    72.33    │   112.26    │
│                        │     quantization_config=None │            │            │              │             │             │             │
│                        │ )                            │            │            │              │             │             │             │
├────────────────────────┼──────────────────────────────┼────────────┼────────────┼──────────────┼─────────────┼─────────────┼─────────────┤
│ Decoder                │ TensorRTBackend(             │   15.43    │    1.81    │      12      │     56      │   100.88    │   148.19    │
│                        │     quantization_config=None │            │            │              │             │             │             │
│                        │ )                            │            │            │              │             │             │             │
├────────────────────────┼──────────────────────────────┼────────────┼────────────┼──────────────┼─────────────┼─────────────┼─────────────┤
│ Decoder                │ TensorRTBackend(             │   15.46    │    1.8     │    33.38     │     60      │   117.22    │   167.79    │
│                        │     use_dynamo=False,        │            │            │              │             │             │             │
│                        │     quantization_config=None │            │            │              │             │             │             │
│                        │ )                            │            │            │              │             │             │             │
├────────────────────────┼──────────────────────────────┼────────────┼────────────┼──────────────┼─────────────┼─────────────┼─────────────┤
│ Decoder                │ TorchInductorJitBackend()    │   15.53    │    1.7     │     3.12     │     85      │    85.92    │   179.21    │
├────────────────────────┼──────────────────────────────┼────────────┼────────────┼──────────────┼─────────────┼─────────────┼─────────────┤
│ FluxTransformer2DModel │ TensorRTBackend(             │   14.36    │    1.79    │      0       │      0      │    67.84    │    71.79    │
│                        │     quantization_config=None │            │            │              │             │             │             │
│                        │ )                            │            │            │              │             │             │             │
├────────────────────────┼──────────────────────────────┼────────────┼────────────┼──────────────┼─────────────┼─────────────┼─────────────┤
│ FluxTransformer2DModel │ TensorRTBackend(             │   14.35    │    1.79    │      0       │      0      │    63.46    │    63.46    │
│                        │     use_dynamo=False,        │            │            │              │             │             │             │
│                        │     quantization_config=None │            │            │              │             │             │             │
│                        │ )                            │            │            │              │             │             │             │
├────────────────────────┼──────────────────────────────┼────────────┼────────────┼──────────────┼─────────────┼─────────────┼─────────────┤
│ FluxTransformer2DModel │ TorchInductorJitBackend()    │   15.53    │    1.79    │     2.44     │     85      │    84.09    │   179.21    │
├────────────────────────┼──────────────────────────────┼────────────┼────────────┼──────────────┼─────────────┼─────────────┼─────────────┤
│ T5EncoderModel         │ TensorRTBackend(             │   16.65    │    1.77    │     1.76     │     85      │    70.57    │   179.21    │
│                        │     quantization_config=None │            │            │              │             │             │             │
│                        │ )                            │            │            │              │             │             │             │
╘════════════════════════╧══════════════════════════════╧════════════╧════════════╧══════════════╧═════════════╧═════════════╧═════════════╛

Combining with NVTX

Hardware metrics and NVTX profiling can be enabled together:

AITUNE_HARDWARE_METRICS=1 AITUNE_NVTX_EVENTS=1 nsys profile -o output.nsys-rep -trace=cuda,nvtx,osrt python your_script.py

Examples

We offer comprehensive examples that showcase the utilization of NVIDIA AITune's diverse features. These examples are designed to elucidate the processes of tuning, profiling, testing, and deployment of models.

For detailed examples and step-by-step guides, please visit our Examples Catalog. The catalog includes practical implementations for various AI workloads including computer vision, natural language processing, speech recognition, and generative AI models.

Useful Links

• Changelog
• Contributing
• License