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ONNX Runtime and CoreML May Silently Convert Your Model to FP16•12/22/2025
5 min read

The Silent Transformation: When ONNX Runtime and Core ML Go FP16 Without You Knowing

The Silent Transformation: When ONNX Runtime and Core ML Go FP16 Without You Knowing

Imagine deploying your meticulously trained machine learning model, only to discover it's subtly, silently, operating in FP16 – a lower precision format. This isn't a hypothetical scenario; it's a reality that can catch developers off guard when using powerful tools like ONNX Runtime and Core ML. This subtle shift, often happening behind the scenes, can have significant implications for your model's accuracy and performance.

Why the Stealthy Switch? The Allure of FP16

Modern hardware, particularly on mobile devices and edge accelerators, is optimized for FP16 (half-precision floating-point) operations. Compared to the standard FP32 (single-precision), FP16 offers several tantalizing benefits:

  • Faster Inference: Calculations in FP16 are inherently quicker, leading to reduced latency and snappier application performance.
  • Lower Memory Footprint: FP16 models occupy less memory, which is crucial for resource-constrained environments like smartphones.
  • Reduced Power Consumption: Less computational effort translates to less power draw, extending battery life.

These advantages are so compelling that frameworks and runtimes often try to leverage them automatically, sometimes without explicit user instruction.

ONNX Runtime: The Flexibility Factor

ONNX Runtime is celebrated for its broad hardware support and optimization capabilities. It strives to deliver the best possible performance on diverse platforms. When you export your model to ONNX and then run it with ONNX Runtime, the runtime environment analyzes your model and the target hardware.

It might then decide that converting some or all of your model's weights and operations to FP16 will yield a significant performance boost. This decision is often driven by the goal of maximizing throughput and minimizing latency, especially on GPUs or NPUs that have dedicated FP16 acceleration.

Core ML: Apple's Performance Focus

On the Apple ecosystem, Core ML is the go-to framework for on-device machine learning. Apple is intensely focused on delivering smooth, power-efficient user experiences. Consequently, Core ML often aggressively optimizes models for its hardware.

When you convert a model to the Core ML format, especially if you don't explicitly specify precision, Core ML might automatically quantize or convert weights to FP16 to take advantage of the Neural Engine or GPU's FP16 capabilities. This is part of its design philosophy to make ML on Apple devices as fast and efficient as possible.

When Does This Matter? Real-World Ripples

This silent conversion isn't always benign. While often beneficial, there are scenarios where it can cause unexpected behavior:

  • Accuracy Degradation: For models that are highly sensitive to numerical precision, a switch to FP16 can lead to a noticeable drop in accuracy. Imagine a medical imaging model that misclassifies a subtle anomaly due to reduced precision.
  • Reproducibility Challenges: If your training and inference environments are not perfectly aligned in terms of precision, debugging can become a nightmare. Results that look good on your development machine might diverge when deployed.
  • Edge Cases and Fine-Tuning: If you've meticulously fine-tuned your model for specific edge cases where minute differences in output matter, an automatic FP16 conversion could inadvertently alter the model's behavior in critical ways.

This is the kind of subtle technical detail that often sparks discussions on platforms like Hacker News, where developers share their experiences and warnings about these kinds of behind-the-scenes optimizations.

Staying in Control: What You Can Do

Don't let your models be silently transformed! Here's how to maintain control:

  • Check the Documentation: Always consult the specific documentation for ONNX Runtime and Core ML regarding precision control. Look for flags or parameters that allow you to specify the desired precision (e.g., fp32, fp16).
  • Profile Your Models: Before deploying, rigorously profile your model's performance and accuracy on the target hardware. Compare results between FP32 and FP16 if possible.
  • Explicitly Define Precision: When exporting or converting your model, look for options to explicitly set the precision of the weights and operations. This gives you the final say.
  • Test Thoroughly: Don't just assume everything is as you expect. Thoroughly test your deployed models with representative datasets to catch any unintended side effects.

The drive for performance is a constant in the ML world. Tools like ONNX Runtime and Core ML are indispensable for achieving that. However, understanding their optimization strategies, especially regarding FP16 conversion, is key to ensuring your models behave exactly as intended, delivering reliable and accurate results wherever they run.