qualcomm/aimet

Health v2 · maintenance only

Flags: no_license

Adoption not part of the score

2688 stars · 460 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

AIMET (AI Model Efficiency Toolkit) is a Python library from Qualcomm providing advanced quantization and compression techniques for trained neural network models. It supports PyTorch and ONNX models, using post-training and fine-tuning techniques to minimize accuracy loss while reducing memory footprint and compute load for edge deployment.

Use cases

• quantize a trained PyTorch model to 8-bit integers
• compress a deep learning model for mobile deployment
• reduce memory footprint of an ONNX model for edge devices
• apply post-training quantization without losing accuracy
• prune channels from a neural network to speed up inference
• prepare a model to run fast on Qualcomm Hexagon DSP

When to choose

• you need to quantize or compress PyTorch or ONNX models for edge/mobile inference
• you want advanced techniques like data-free quantization or cross-layer equalization to preserve accuracy
• you target Qualcomm hardware such as Hexagon DSP

When to avoid

• you need quantization-aware training for frameworks other than PyTorch or ONNX, such as TensorFlow
• you just want a simple one-line quantization API without tuning options
• your project is unrelated to model efficiency or deployment optimization

Facets

library · maturity active

machine-learning deep-learning llm-training machine-learning deep-learning gpu-computing python quantization model-compression pruning post-training-quantization pytorch onnx edge-deployment model-optimization linux gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="qualcomm/aimet")