Intel® Neural Compressor
Speed Up AI Inference without Sacrificing Accuracy
Deploy More Efficient Deep Learning Models
Intel® Neural Compressor performs model optimization to reduce the model size and increase the speed of deep learning inference for deployment on CPUs, GPUs, or Intel® Gaudi® AI accelerators. This open source Python* library automates popular model optimization technologies, such as quantization, pruning, and knowledge distillation across multiple deep learning frameworks.
Using this library, you can:
- Converge quickly on quantized models through automatic accuracy-driven tuning strategies.
- Prune the least important parameters for large models.
- Distill knowledge from a larger model to improve the accuracy of a smaller model for deployment.
Intel Neural Compressor is part of the end-to-end suite of Intel® AI and machine learning development tools and resources.
Download the AI Tools
Intel Neural Compressor is available in the AI Tools Selector, which provides accelerated machine learning and data analytics pipelines with optimized deep learning frameworks and high-performing Python* libraries.
Download the Stand-Alone Version
A stand-alone download of Intel Neural Compressor is available. You can download binaries from Intel or choose your preferred repository.
Help Intel Neural Compressor Evolve
This open source component has an active developer community. We welcome you to participate.
Features
Model Optimization Techniques
- Quantize activations and weights to int8, FP8, or a mixture of FP32, FP16, FP8, bfloat16, and int8 to reduce model size and to speed inference while minimizing precision loss. Quantize during training, posttraining, or dynamically, based on the runtime data range.
- Prune parameters that have minimal effect on accuracy to reduce the size of a model. Configure pruning patterns, criteria, and schedule.
- Automatically tune quantization and pruning to meet accuracy goals.
- Distill knowledge from a larger model (“teacher”) to a smaller model (“student”) to improve the accuracy of the compressed model.
- Customize quantization with advanced techniques such as SmoothQuant, layer-wise quantization, and weight-only quantization (WOQ) for low-bit inference.
Automation
- Achieve objectives with expected accuracy criteria using built-in strategies to automatically apply quantization techniques to operations.
- Combine multiple model optimization techniques with one-shot optimization orchestration.
Interoperability
- Optimize and export PyTorch* or TensorFlow* models.
- Optimize and export Open Neural Network Exchange (ONNX*) Runtime models with Intel Neural Compressor 2.x. As of version 3.x, Intel Neural Compressor is upstreamed into open source ONNX for built-in cross-platform deployment.
- Use familiar PyTorch, TensorFlow, or Hugging Face* Transformer style APIs to configure and autotune model compression.
Case Studies
Palo Alto Networks Reduces Inference Latency by 6x
To deliver the required response speed for multiple cybersecurity models, Palo Alto Networks quantized their models to int8, taking advantage of advanced instruction sets and accelerators.
Sustainable AI with Intel®-Optimized Software and Hardware
HPE Services applied Intel AI software together with int8 post-training static quantization to reduce energy consumption by at least 68% across multiple experiments.
delphai* Accelerates Natural Language Processing Models for Search Engines
By quantizing its models to int8, delphai* accelerated inference speed without sacrificing accuracy, enabling the use of less costly CPU-based cloud instances.
Demonstrations
Microscaling (MX) Quantization
Quantize Microsoft* Floating Point (MSFP) data types to 8-, 6-, or 4-bit MX data types while balancing accuracy and memory consumption.
The AutoRound Quantization Algorithm
Achieve near-lossless weight-only quantization (WOQ) compression for popular large language models (LLMs).
Quantize LLMs with SmoothQuant
LLMs tend to have large-magnitude outliers in certain activation channels. Learn how the SmoothQuant technique addresses this and how to use it to quantize a Hugging Face* Transformer model to 8-bit.
Quantize Large Language Models with Just a Few Lines of Code
Quantizing LLMs to int4 reduces model size up to 8x, speeding inference. Learn how to get started applying weight-only quantization (WOQ) and see the accuracy impact on popular LLMs.
Distill and Quantize BERT Text Classification
Perform knowledge distillation of the BERT base model and quantize to int8 using the Stanford Sentiment Treebank 2 (SST-2) dataset. The resulting BERT-Mini model performs inference up to 16x faster.
Quantization in PyTorch Using Fine-Grained FX
Convert an imperative model into a graph model, and perform dynamic quantization, quantization-aware training, or post training static quantization.
Documentation & Code Samples
Specifications
Processor:
- Intel Xeon processor
- Intel Xeon CPU Max Series
- Intel® Core™ Ultra processor
- Intel Gaudi AI accelerator
- Intel® Data Center GPU Max Series
Operating systems:
- Linux*
- Windows*
Language:
- Python
Get Help
Your success is our success. Access this support resource when you need assistance.
For additional help, see the general Support.
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