NVIDIA L4 CUDA Performance Guide: Specs, Benchmarks & Optimization

Introduction

The NVIDIA L4 is the next-generation inference GPU designed to replace the ubiquitous T4. Built on Ada Lovelace architecture with 24GB GDDR6 memory and just 72W TDP, the L4 delivers up to 3x the inference performance of T4 while maintaining the same power envelope and form factor. For CUDA developers, the L4 brings 4th generation Tensor Cores with FP8 support to the inference tier. The combination of modern architecture, increased memory, and new precision formats makes it ideal for deploying generative AI models including Stable Diffusion and small LLMs. This guide covers the L4's specifications, CUDA optimization strategies, benchmark results, and practical tips for maximizing inference performance.

Specifications

Key Features

• 72W TDP - same as T4
• 24GB GDDR6 - 50% more than T4
• 4th Gen Tensor Cores with FP8
• 3x faster inference than T4
• Low-profile single-slot design
• PCIe 4.0 x16 interface
• CUDA Compute Capability 8.9
• AV1 hardware encoder
• Transformer Engine support
• 48MB L2 cache

CUDA Optimization Tips

• 1.Use FP8 precision for maximum inference throughput
• 2.Leverage the 48MB L2 cache for attention mechanisms
• 3.Target 24GB for larger models vs T4
• 4.Use INT8 for legacy model compatibility
• 5.Profile with Nsight Compute for Ada optimizations
• 6.Use TensorRT 9+ for FP8 support
• 7.Batch requests to maximize Tensor Core utilization
• 8.Consider L4 for Stable Diffusion production
• 9.Use CUDA graphs for repetitive inference patterns
• 10.Optimize for the large L2 cache with tiled algorithms

Code Examples

L4 Setup and Memory Check

This code snippet shows how to detect your L4, check available memory, and configure optimal settings for the Ada Lovelace (AD104) architecture.

import torch
import pynvml

# Check if L4 is available
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(f"Using device: {torch.cuda.get_device_name(0)}")

# L4 Memory: 24GB - Optimal batch sizes
# Architecture: Ada Lovelace (AD104)
# CUDA Cores: 7,424

# Memory-efficient training for L4
torch.backends.cuda.matmul.allow_tf32 = True  # Enable TF32 for Ada Lovelace (AD104)
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 / 24 GB total")

# Recommended batch size calculation for L4
model_memory_gb = 2.0  # Adjust based on your model
batch_multiplier = (24 - model_memory_gb) / 4  # 4GB per batch unit
recommended_batch = int(batch_multiplier * 32)
print(f"Recommended batch size for L4: {recommended_batch}")

Benchmarks

Use Cases

Pros and Cons

Frequently Asked Questions

Alternatives

Ready to optimize your CUDA kernels for L4? Download RightNow AI for real-time performance analysis.