GPU-Accelerated Optimization of Transformer-Based Neural Networks for Real-Time Inference

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• The GPU-accelerated inference pipeline achieves up to 64.4x speedup over CPU baselines.result 95%
• The system achieves sub-10 ms latency for single-sample inference.result 95%
• A hybrid precision strategy using FP16 for linear layers and FP32 for softmax and layer normalization maintains high numerical fidelity (cosine similarity >= 0.9998 relative to baseline outputs).method 90%
• The hybrid precision strategy eliminates NaN instability that can occur with full FP16 precision.technical 90%
• Validation on the SST-2 dataset shows no accuracy degradation when using the hybrid precision inference pipeline.result 90%
• Using random inputs for testing can underestimate NaN instability by up to 6x for full FP16 precision compared to using real data (WikiText-2).technical 85%
• The pipeline implementation is modular and containerized, enabling reproducible benchmarking across more than 360 configurations.method 90%
• Cross-GPU validation on an NVIDIA A100 shows consistent FP16 speedup ratios between 1.84x and 2.00x.result 85%

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