NVIDIA Tesla T4 Review | 118 Data compared

Dell Support Article 000132094 evaluates NVIDIA T4 Tensor Core GPU training performance using MLPerf benchmarks on a PowerEdge R740 server, comparing up to four T4 GPUs against V100-PCIe configurations across tasks like ResNet-50 and Transformer. The study shows that while the single-width T4 offers superior scaling and energy efficiency, allowing higher density per server, it yields 2.2x to 3.6x lower speed and throughput compared to the high-end V100-PCIe. Pros include the T4’s small form factor enabling up to four cards per server and consistent stability in models like ResNet-50, while cons include lower raw processing power and high sensitivity to random seed values, leading to varied convergence on models like GNMT and NCF. The T4 is positioned as a viable, cost-effective option for specific inference and lighter training workloads, despite requiring more tuning compared to the V100.

In this review, Oliver Wehrens evaluates the performance of the OpenAI Whisper "large" model for transcribing a 2:01-hour (7,250 seconds) audio file, comparing the Nvidia Tesla T4 and the Nvidia Tesla A100 GPUs. Testing on Google Colab, the Tesla T4 required 794 seconds to complete the transcription, achieving a 9.2x speed increase relative to real-time audio playback. Stepping up to a Google Cloud instance, the Tesla A100 completed the task in 710 seconds, delivering a 10.2x speed increase. Wehrens notes that despite the A100 possessing vastly superior FP16 hardware specifications (up to 312/624 TFLOPS compared to the T4's 65 TFLOPS), the actual performance scaling for Whisper transcription is surprisingly narrow, likely due to a low CPU utilization rate of only 40% on the cloud instance during the process. The primary pros highlighted in the review include the high efficiency of the cost-effective Tesla T4, which performs remarkably close to the premium cloud tier, and the capability of both cards to deliver swift, sub-15-minute execution times for long-form audio files. Furthermore, a significant advantage for users is the ability to leverage...

The ServeTheHome analysis identifies the NVIDIA Tesla T4 as a flexible, Turing-based GPU designed for AI inference, featuring 16GB of ECC memory and INT4 capabilities. Pros include its low-profile, single-slot form factor, 70W power consumption via the PCIe slot, and the ability to pack multiple units into 1U/2U servers without requiring external power. Conversely, the T4's cons involve its high $2,000–$2,500 price point and lower raw FP16/FP32 performance compared to consumer cards like the RTX 2060 Super. Additionally, the card is passively cooled, necessitating high server airflow, and it often requires multiple units to match the raw compute power of cheaper alternatives.

This Medium review benchmarks AI performance, showing that CPUs are unsuitable for LLM workloads, with the RTX 4090 offering the best price-to-performance for training and inference. While the Tesla T4 provides a cost-effective entry point with a 15x to 50x speed boost over CPUs, the RTX 5090 underperformed in tests due to optimization issues. Medium ·Mehul Gupta The main advantage of CPUs is accessibility, but they suffer from a lack of parallel efficiency, while GPUs offer superior speed for AI tasks. The RTX 4090 serves as a high-performance, cost-effective option, contrasting with the slower-than-expected, unoptimized performance of the RTX 5090. Medium ·

Tharindu Sankalpa's benchmark of Qwen models across NVIDIA T4, L4, and H100 GPUs highlights predictable scalability, with the H100 delivering high-speed, 50+ token-per-second performance for heavy workloads. The mid-tier L4 GPU is identified as the optimal, cost-efficient solution, providing a stable 30+ tokens per second at a moderate cost for 7B models. Conversely, the T4 represents a performance bottleneck with slow token generation suitable only for batch processing, while the H100’s high cost-to-performance ratio makes it unnecessary for smaller models. The review notes that improperly matching hardware, specifically using the T4 for real-time tasks, severely hampers interactivity.

The Dell technical article evaluates the NVIDIA T4 Tensor Core GPU using the MLPerf v0.5 suite on a PowerEdge R740, highlighting versatile support across AI workloads and efficient, high-density scaling for multiple cards. However, the T4 exhibited significant training instability in certain models (GNMT, NCF, Transformer) and, while suitable for lower-scale tasks, was outpaced 2.2x to 3.6x in raw throughput by the V100-PCIe, indicating a major performance drawback for heavy training. Despite the speed gap, the T4 offers predictable scalability, with performance increasing as more cards are added, making it a viable, cost-effective option for specific, multi-GPU workloads.

The Microway review provides a comprehensive performance benchmark of the NVIDIA Tesla T4 GPU, comparing it to the high-end Tesla P100 and V100 models using the Scalable Heterogeneous Computing (SHOC) benchmark suite. The evaluation highlights the T4's design as an incredibly cost-effective, energy-efficient accelerator tailored specifically for machine learning inference and single-precision scientific computing. Operating at a mere 70 Watts and priced at around $2,500, the T4 delivers highly competitive single-precision performance. In specific benchmarks like Molecular Dynamics, it reaches over half the performance of the premium V100 and actually beats the P100. Furthermore, in Fast Fourier Transform (FFT) and Matrix Multiplication tests, it achieves exactly one-fourth of the V100's performance for a matching one-fourth of both the price and power consumption. However, the review outlines clear architectural trade-offs, positioning specific pros and cons for data center deployments. On the positive side (pros), the T4 boasts exceptional energy efficiency, lower acquisition and operational costs, and a small physical form factor with a single-slot passive heatsink that easily fits into a wider variety of scale-out servers. It also introduces advanced "Turing"...

The ServeTheHome review highlights the NVIDIA Tesla T4 as a highly efficient, 70W, single-slot, low-profile PCIe accelerator designed for enterprise AI inference, utilizing 16GB of ECC GDDR6 memory. Its key advantages include exceptional deployment density, reduced cable clutter, and improved server airflow, alongside significant INT4/INT8 throughput (130 TOPS) that allows for flexible, low-power multi-GPU configurations, often surpassing larger accelerators in optimized AI tasks. Conversely, the review notes that the Tesla T4's performance in raw FP16/FP32 benchmarks is lower than consumer-grade cards like the RTX 2060 Super, driven by low base clock speeds meant to manage power. It also commands a high enterprise premium, often costing over $2,000, and relies solely on server-level passive cooling, requiring robust, dedicated airflow to avoid thermal throttling.