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, and Torch Inductor 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.
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, and Torch Inductor |
| 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 and Torch-TensorRT |
| 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 |
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.
Before proceeding with the installation of NVIDIA AITune, ensure your system meets the following criteria:
- Operating System: Linux (Ubuntu 22.04+ recommended)
- Python: Version
3.10or newer - PyTorch: Version
2.7or newer - TensorRT: Version
10.5.0or higher (for TensorRT backend) - NVIDIA GPU: Required for GPU-accelerated tuning
You can use NGC Containers for PyTorch which contain all necessary dependencies:
NVIDIA AITune can be installed from pypi.org.
pip install --extra-index-url https://pypi.nvidia.com aitune# 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 .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
inspectto 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.
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)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 torchThen 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")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<5" diffusers torchPrepare 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.pyNote: 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.
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
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.backends = [TensorRTBackend()] # change the backendsThe 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) 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.
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)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")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),
)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.txtYou 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.
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.
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.
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)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 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 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 leverages PyTorch's AO (Accelerated Optimization) framework for model tuning.
from aitune.torch.backend import TorchAOBackend
backend = TorchAOBackend()Torch Inductor backend uses PyTorch's Inductor compiler for model tuning.
from aitune.torch.backend import TorchInductorBackend
backend = TorchInductorBackend()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.
Tries backends in priority order and returns the first one that succeeds. If a backend fails, the strategy moves on to the next candidate instead of aborting.
from aitune.torch.tune_strategy import FirstWinsStrategy
strategy = FirstWinsStrategy(backends=[TensorRTBackend(), TorchInductorBackend()])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())Profiles all compatible backends and selects the fastest. Use this when maximum throughput matters and you can afford longer tuning time.
from aitune.torch.tune_strategy import HighestThroughputStrategy
strategy = HighestThroughputStrategy(backends=[TensorRTBackend(), TorchInductorBackend(), TorchEagerBackend()])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.
To enable NVTX profiling, set the environment variable before running your script:
export NVTX_ENABLE=1
python your_script.pyOnce enabled, you can profile your application with Nsight Systems:
NVTX_ENABLE=1 nsys profile -o output.nsys-rep python your_script.pyThe 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
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.