DEEP-GAP: Deep-learning Evaluation of Execution Parallelism in GPU Architectural Performance
Modern datacenters increasingly rely on low-power, single-slot inference accelerators to balance performance, energy efficiency, and rack density constraints. The NVIDIA T4 GPU has become widely deployed due to strong performance per watt and mature software support. Its successor, the NVIDIA L4 GPU, introduces improvements in Tensor Core throughput, cache capacity, memory bandwidth, and parallel execution capability. However, limited empirical evidence quantifies the practical inference performance gap between these two generations under controlled and reproducible conditions. This work introduces DEEP-GAP, a systematic evaluation extending the GDEV-AI methodology to GPU inference. Using...
| Authors | Kathiravan Palaniappan |
|---|---|
| Primary category | cs.PF |
| Published | 16 April 2026 |
| Last updated at source | 6 May 2026 |
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