VoltaML/voltaML

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1176 stars · 38 forks observed · 2026-08-28

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

VoltaML is a lightweight Python library that compiles and optimizes machine learning and deep learning models for high-performance inference runtimes such as TensorRT, TorchScript, ONNX, and TVM. It supports FP16/Int8 quantization and hardware-specific compilation for CPU and GPU targets with a one-line-of-code API.

Use cases

• convert pytorch models to tensorrt for faster inference
• accelerate deep learning model inference on nvidia gpus
• quantize models to fp16 or int8
• compile models for cpu with tvm
• benchmark compiled model performance against original
• deploy optimized models to production servers

When to choose

• you need faster inference for pytorch models on NVIDIA GPUs
• you want a simple one-line API to compile models to TensorRT, ONNX, TorchScript, or TVM
• you want to quantize models to fp16 or int8 without manual runtime plumbing

When to avoid

• you need a maintained tool - the last release was in 2022 and the project appears inactive
• you are not on Linux with CUDA 11.x, TensorRT 8.4, and PyTorch 1.12
• you need CPU-only inference on non-x86 targets or non-NVIDIA accelerators
• you need broad model architecture coverage or recent framework versions

Facets

library · maturity maintenance

machine-learning deep-learning llm-inference compiler benchmarking machine-learning deep-learning gpu-computing performance developer-tools python model-compilation inference-optimization tensorrt onnx torchscript tvm quantization model-acceleration linux docker gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="VoltaML/voltaML")