AI Inference Optimization

Model Speed
Comparator

Compare PyTorch baseline vs ONNX vs INT8 Quantized inference — same model, same prediction, dramatically different performance.

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Speedup achieved — Quantized INT8 vs PyTorch baseline
Smallest model: —  ·  Fastest: —
Session Stats
Latency Comparison (ms) — lower is better
Live Latency Trend (last 10 runs)
Model Size (MB) — lower is better

What This Demonstrates

ONNX Export — Converts PyTorch model to a hardware-agnostic format. ONNX Runtime applies graph optimizations (operator fusion, memory planning) that PyTorch doesn't do by default.
INT8 Quantization — Reduces weight precision from 32-bit floats to 8-bit integers. 4x smaller model, faster memory bandwidth, same accuracy on most NLP tasks.
Why it matters — AI accelerator teams (like HCL's) optimize model inference for deployment at scale. These techniques are the foundation of production ML systems.
Recent Comparisons
Time Input Fastest Speedup Baseline ONNX Quantized Copy
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