NVIDIA T4 CUDA Performance Guide: Specs, Benchmarks & Optimization

Introduction

The NVIDIA T4 is the most widely deployed inference GPU in cloud computing, offering an exceptional balance of performance, power efficiency, and cost. Built on Turing architecture with 16GB GDDR6 memory and just 70W TDP, the T4 fits in standard server form factors without requiring additional power connectors. For CUDA developers, the T4's 2nd generation Tensor Cores provide excellent INT8 and FP16 inference performance. Its ubiquitous availability across all major cloud providers makes it the default choice for deploying ML models at scale. The low power consumption enables high-density deployments with multiple T4s per server. This guide covers the T4's specifications, CUDA optimization strategies, benchmark results, and practical tips for maximizing inference performance.

Specifications

Key Features

• 70W TDP - no external power needed
• 16GB GDDR6 memory
• Low-profile single-slot design
• 2nd Gen Tensor Cores with INT8
• 130 INT8 TOPS for inference
• Available on all major clouds
• PCIe 3.0 x16 interface
• CUDA Compute Capability 7.5
• TensorRT optimized
• Multi-instance GPU (MIG) via software

CUDA Optimization Tips

• 1.Use INT8 quantization with TensorRT for maximum throughput
• 2.Batch requests to maximize Tensor Core utilization
• 3.The 70W TDP means thermal throttling is rare - stable performance
• 4.Use FP16 for accuracy-sensitive inference
• 5.Profile with Nsight for memory bandwidth optimization
• 6.Consider multiple T4s per server for scaling
• 7.Use CUDA streams for concurrent inference
• 8.Optimize batch sizes for the 16GB memory limit
• 9.Leverage TensorRT for automatic kernel optimization
• 10.Use dynamic batching for variable-length inputs

Code Examples

T4 Setup and Memory Check

This code snippet shows how to detect your T4, check available memory, and configure optimal settings for the Turing (TU104) architecture.

import torch
import pynvml

# Check if T4 is available
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(f"Using device: {torch.cuda.get_device_name(0)}")

# T4 Memory: 16GB - Optimal batch sizes
# Architecture: Turing (TU104)
# CUDA Cores: 2,560

# Memory-efficient training for T4
torch.backends.cuda.matmul.allow_tf32 = True  # Enable TF32 for Turing (TU104)
torch.backends.cudnn.allow_tf32 = True

# Check available memory
pynvml.nvmlInit()
handle = pynvml.nvmlDeviceGetHandleByIndex(0)
info = pynvml.nvmlDeviceGetMemoryInfo(handle)
print(f"Free memory: {info.free / 1024**3:.1f} GB / 16 GB total")

# Recommended batch size calculation for T4
model_memory_gb = 2.0  # Adjust based on your model
batch_multiplier = (16 - model_memory_gb) / 4  # 4GB per batch unit
recommended_batch = int(batch_multiplier * 32)
print(f"Recommended batch size for T4: {recommended_batch}")

Benchmarks

Use Cases

Pros and Cons

Frequently Asked Questions

Alternatives

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