Model Quantization Testing for Edge AI: FP32 vs INT8 on Real Hardware

What Is Model Quantization and Why Does Every Edge AI Team Use It?

Quantization reduces the numerical precision of a model's weights and activations — typically from 32-bit floating point (FP32) to 8-bit integers (INT8). The benefits are significant: smaller model files, lower memory usage, and often faster inference on hardware accelerators designed for integer math.

For edge deployment, quantization isn't optional. A model running on a Snapdragon 8 Gen 3 in a mobile or robotics application has hard constraints: limited memory, power budget, and latency requirements that FP32 models often can't meet. INT8 quantization is the standard path to fitting within those constraints.

The catch is that quantization changes the model's behavior in ways that are difficult to predict from cloud-side analysis alone. The same quantization that works perfectly on one chipset can introduce regressions on another, because the underlying NPU operator implementations differ.

What Are the Four Types of Quantization Regression?

When you quantize a model for edge deployment, four categories of regression can emerge. Each manifests differently on real hardware versus cloud simulations.

The operator fallback problem is particularly insidious. Your model file is smaller, your cloud benchmarks look good, but on the actual device, a single unsupported INT8 operator forces the entire subgraph to execute on the CPU. Latency jumps from sub-millisecond to tens of milliseconds, and you only discover it when users complain.

What Does Real-World FP32 vs INT8 Data Look Like?

We tested a person-detection model (MobileNet-style depthwise separable CNN) on real Snapdragon 8 Gen 3 hardware via Qualcomm AI Hub. Both FP32 and INT8 variants were compiled, deployed, and profiled on a Samsung Galaxy S24 family device.

Both models passed all gates. The INT8 variant achieved a 70.5% reduction in model file size while maintaining comparable inference latency. Notably, the INT8 model used slightly more peak memory (124.66 MB vs 121.51 MB) — a counterintuitive result that illustrates why you can't assume quantization always reduces every metric.

The memory increase happens because INT8 models sometimes require additional dequantization buffers and different tensor layout alignment. This is exactly the kind of regression that cloud-only benchmarking misses.

How Should You Structure Quantization Quality Gates?

Effective quantization testing requires comparing the quantized model against its full-precision baseline on the same physical device under the same conditions. Here's a gate structure we recommend:

The relative latency gate is critical. If your INT8 model is slower than FP32, something is wrong — most likely operator fallback. This gate catches the problem immediately.

Why Can't You Test Quantization in the Cloud?

Cloud-based quantization tools (ONNX Runtime, TensorRT, PyTorch quantization) give you a quantized model and accuracy metrics. What they can't tell you is how that model behaves on a specific NPU with a specific firmware version.

The Snapdragon 8 Gen 3's Hexagon NPU has a specific set of INT8 operators it accelerates natively. When a quantized model uses an operator outside that set, the runtime silently falls back to CPU execution. This fallback doesn't appear in any cloud benchmark — it only shows up when you profile on the actual device.

Similarly, memory behavior varies between chipsets. The same INT8 model might use 120 MB on a Snapdragon 8 Gen 3 but 180 MB on a Snapdragon 7+ Gen 2 due to different tensor layout requirements. Without testing on each target device, you're guessing.

How Do You Automate Quantization Testing with EdgeGate?

EdgeGate automates the full quantization testing workflow:

1. Upload both variants: Push your FP32 and INT8 ONNX models to the same pipeline. EdgeGate compiles both for your target Snapdragon device via Qualcomm AI Hub.
2. Run on real hardware: Both models execute on the same physical device, eliminating device-to-device variance from the comparison.
3. Gate evaluation: Results are compared against absolute thresholds and relative thresholds (INT8 vs FP32). You get a clear PASS or FAIL for each gate.
4. Evidence bundles: Both runs produce Ed25519-signed evidence reports with model SHA-256 hashes, device attestation, and raw performance data. Tamper-proof proof of what happened.
5. CI integration: Wire this into GitHub Actions so every PR that changes model files or quantization configs triggers a comparison test automatically.

What Are Best Practices for Quantization Testing on Snapdragon?

After running hundreds of quantization comparisons on real Snapdragon hardware, here are the patterns that matter:

• Always test both variants on the same device, same session. Device-to-device and session-to-session variance can mask real regressions.
• Use warmup iterations. The first 2–3 runs on a cold device include NPU initialization overhead. Exclude them from your measurements.
• Set the relative latency gate tight. INT8 should be equal to or faster than FP32. If it's even 20% slower, investigate operator fallback.
• Monitor model file size as a sanity check. INT8 models should be 60–75% smaller. If the reduction is less, some weights may not have quantized correctly.
• Test across your full device matrix. Quantization behavior varies by chipset. A model that passes on Snapdragon 8 Gen 3 may fail on Snapdragon 7+ Gen 2.
• Track trends over time. A single quantization run is a snapshot. Track latency and memory across model versions to catch gradual drift.