DEEP-GAP: Deep-learning Evaluation of Execution Parallelism in GPU Architectural Performance

2026-04-16 · Source: Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure · Depth: Intermediate, quick

Summary

The DEEP-GAP evaluation framework systematically quantifies the inference performance differences between NVIDIA T4 and L4 GPUs, extending the GDEV-AI methodology. This study focuses on low-power, single-slot accelerators critical for modern datacenters, assessing ResNet18, ResNet50, and ResNet101 models across FP32, FP16, and INT8 precision modes using PyTorch and TensorRT. Key findings indicate that INT8 precision dramatically boosts throughput by up to 58x compared to CPU baselines. The NVIDIA L4 GPU achieves up to 4.4x higher throughput than the T4, demonstrating peak efficiency at smaller batch sizes (16-32), which benefits latency-sensitive applications. The T4 remains a viable option for larger batch workloads where cost or power efficiency is a primary concern.

Key takeaway

For AI Architects and Computer Vision Engineers optimizing inference deployments, your choice between NVIDIA T4 and L4 GPUs should align with workload characteristics. If you prioritize low latency and high throughput for smaller batch sizes (16-32), the L4 offers significant advantages, up to 4.4x faster. Conversely, if your applications involve large batch sizes and are more sensitive to cost or power efficiency, the T4 remains a competitive and practical solution.

Key insights

NVIDIA L4 GPUs offer up to 4.4x higher inference throughput than T4, excelling at smaller batch sizes.

Principles

• Reduced precision significantly improves inference performance.
• L4 GPUs are more efficient at smaller batch sizes (16-32).
• T4 GPUs remain competitive for large batch workloads.

Method

DEEP-GAP extends GDEV-AI methodology to evaluate GPU inference using identical configurations and workloads across multiple ResNet models and precision modes.

In practice

• Use INT8 precision for up to 58x throughput gains.
• Select L4 for latency-sensitive inference at small batch sizes.
• Consider T4 for large batch, cost-sensitive deployments.

Topics

• DEEP-GAP
• GPU Inference
• NVIDIA L4 GPU
• NVIDIA T4 GPU
• Reduced Precision

Best for: AI Architect, Computer Vision Engineer, CTO, Machine Learning Engineer, AI Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.