NVIDIA L40 CUDA Performance Guide: Specs, Benchmarks & Optimization

Introduction

The NVIDIA L40 brings Ada Lovelace architecture to professional visualization and AI workloads with 48GB of GDDR6 memory. Positioned between the consumer RTX 4090 and datacenter L40S, the L40 combines RT cores for ray tracing with Tensor Cores for AI acceleration. For CUDA developers, the L40 offers a versatile platform that handles both graphics and compute workloads. The 48GB memory capacity enables large model inference and complex rendering scenes, while maintaining reasonable power consumption at 300W. This guide covers the L40's specifications, CUDA optimization strategies, benchmark results, and practical tips for maximizing performance.

Specifications

Key Features

• 48GB GDDR6 memory
• 18,176 CUDA cores
• 4th Gen Tensor Cores
• 3rd Gen RT Cores for ray tracing
• 96MB L2 cache
• PCIe 4.0 x16 interface
• CUDA Compute Capability 8.9
• AV1/HEVC/H.264 encode/decode
• Professional drivers
• vGPU support

CUDA Optimization Tips

• 1.Use FP8 for maximum inference throughput
• 2.Leverage 96MB L2 cache for memory-bound kernels
• 3.The 48GB enables larger batch sizes
• 4.Profile RT+compute hybrid workloads carefully
• 5.Use mixed precision for training workloads
• 6.Consider L40 for visual computing + AI pipelines
• 7.Optimize memory bandwidth utilization
• 8.Use CUDA streams for concurrent graphics and compute
• 9.Target the Ada SM architecture for best performance
• 10.Use professional drivers for stability

Code Examples

L40 Setup and Memory Check

This code snippet shows how to detect your L40, check available memory, and configure optimal settings for the Ada Lovelace (AD102) architecture.

import torch
import pynvml

# Check if L40 is available
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(f"Using device: {torch.cuda.get_device_name(0)}")

# L40 Memory: 48GB - Optimal batch sizes
# Architecture: Ada Lovelace (AD102)
# CUDA Cores: 18,176

# Memory-efficient training for L40
torch.backends.cuda.matmul.allow_tf32 = True  # Enable TF32 for Ada Lovelace (AD102)
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 / 48 GB total")

# Recommended batch size calculation for L40
model_memory_gb = 2.0  # Adjust based on your model
batch_multiplier = (48 - model_memory_gb) / 4  # 4GB per batch unit
recommended_batch = int(batch_multiplier * 32)
print(f"Recommended batch size for L40: {recommended_batch}")

Benchmarks

Use Cases

Pros and Cons

Frequently Asked Questions

Alternatives

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