What Are the Main Technical Specifications of the NVIDIA L40S GPU?

The NVIDIA L40S GPU is one of the most advanced graphics processing units designed for high-performance AI workloads, graphics rendering, and virtualization. Built on the Ada Lovelace architecture, it bridges the gap between AI computation and visual computing, providing both flexibility and power for researchers, developers, and enterprises.

This blog provides a detailed overview of the technical specifications of the NVIDIA L40S GPU, its performance metrics, core features, and the real-world applications it supports.

Introduction to NVIDIA L40S

The NVIDIA L40S is engineered to handle AI-trained models, generative AI models, real-time graphics, and virtual workstation tasks. It is designed to work efficiently in enterprise data centers, research labs, and creative studios.

Key advantages of the L40S include:

• High memory capacity for large AI models
• Advanced GPU cores for AI inference and training
• Ray tracing and rendering capabilities for graphics
• Support for virtualization and multi-user workloads

Architecture Overview

The L40S is based on NVIDIA’s Ada Lovelace architecture, which is optimized for both AI workloads and graphics-intensive applications.

• CUDA Cores: 18,176
• Tensor Cores: 568 (4th Generation)
• RT (Ray Tracing) Cores: 142
• Memory: 48 GB GDDR6 with ECC
• Memory Bandwidth: 864 GB/s
• Form Factor: Dual-slot, full-height
• Interface: PCIe Gen4 x16
• Power Consumption: 350W
• Thermal Solution: Passive
• Display Outputs: 4 x DisplayPort 1.4a

This architecture provides the L40S with exceptional parallel processing capabilities, enabling both AI inference and high-quality graphics rendering simultaneously.

Performance Metrics

The L40S GPU delivers outstanding performance across multiple precision formats, making it suitable for AI, deep learning, and graphics workloads:

• FP32 (Single Precision): ~91.6 TFLOPS
• TF32 Tensor Core: ~183 TFLOPS (with sparsity)
• FP16 / BF16 Tensor Core: ~366 TFLOPS (with sparsity)
• FP8 Tensor Core: ~733 TFLOPS (with sparsity)
• INT8 Tensor Core: ~1466 TOPS

These specifications make the L40S highly capable of running large language models (LLMs), generative AI models, and computationally intensive graphics applications without bottlenecks.

Key Features

• Ada Lovelace Architecture:
  The Ada Lovelace architecture is designed to enhance AI and graphics processing. It improves energy efficiency while increasing compute power, making it ideal for enterprise workloads that require sustained high performance.
• Fourth-Generation Tensor Cores:
  The L40S features 4th Gen Tensor Cores optimized for multiple precision formats including FP8, FP16, BF16, and FP32. These cores accelerate AI workloads such as deep learning training, inference, and generative AI model deployment.
• Ray Tracing and Rendering:
  With 142 RT cores, the L40S provides realistic lighting, shadows, and reflections for graphics applications. This capability is valuable for design, simulation, and gaming applications that demand high-fidelity visuals.
• Virtual GPU (vGPU) Support:
  The L40S supports virtualization, allowing multiple virtual machines to share the GPU efficiently. This is particularly useful for organizations offering virtual desktops, remote workstations, or cloud-based AI services.
• Secure Boot and Root of Trust:
  The GPU includes security features like secure boot and root of trust, ensuring the integrity of firmware and software. This makes it reliable for enterprise deployments handling sensitive data.
• DLSS (Deep Learning Super Sampling) Support:
  DLSS leverages AI to upscale lower-resolution images in real time, improving graphics performance without sacrificing visual quality.

Applications of NVIDIA L40S

The NVIDIA L40S is versatile and can handle a wide range of applications:

• AI and Deep Learning:
  • Supports large-scale AI model training
  • Accelerates inference for pre-trained AI models
  • Handles generative AI applications for content creation
• Large Language Models (LLMs):
  • Provides sufficient memory and tensor core performance for LLM inference and fine-tuning
  • Enables low-latency response for chatbots and AI assistants
• Graphics Rendering:
  • Real-time rendering for 3D design and simulations
  • Advanced visual effects for entertainment and media
• Virtual Workstations:
  • vGPU support allows multiple users to access GPU resources
  • Enables remote work and collaboration without compromising performance
• Video Processing:
  • Accelerates encoding, decoding, and streaming of high-resolution video content
  • Supports AI-based video enhancements

Comparison with Other GPUs

Compared to NVIDIA’s H100 and A100 GPUs:

GPU Model	Architecture	Memory	CUDA Cores	Tensor Cores	Use Case
L40S	Ada Lovelace	48 GB GDDR6	18,176	568	AI inference + graphics
H100	Hopper	80 GB HBM3	14,592	640	Large-scale AI training
A100	Ampere	40–80 GB HBM2e	6,912	432	General AI workloads

The L40S stands out for mixed workloads, balancing AI inference, graphics rendering, and virtualization, whereas H100 focuses on high-throughput AI training and A100 offers cost-effective general-purpose AI computation.

Why the L40S is Important

• High performance for both AI and graphics in a single GPU
• Memory-intensive AI workflows, such as generative AI
• Virtualization support for remote and multi-user environments
• Scalable AI infrastructure without investing in multiple specialized GPUs

Its combination of compute, memory, and versatility makes it a preferred choice for enterprises, researchers, developers, and creative professionals.

Conclusion

The NVIDIA L40S GPU offers a unique blend of power, flexibility, and performance. With its Ada Lovelace architecture, advanced tensor cores, ray tracing capabilities, and virtualization support, it can handle a variety of demanding AI and graphics workloads efficiently.

At Cyfuture AI, we leverage L40S GPUs to provide high-performance AI infrastructure for training, inference, generative AI, and virtual workstations. Whether you are a student, researcher, developer, or enterprise, Cyfuture AI ensures your AI pipelines and graphics workloads run smoothly and cost-effectively.

By choosing L40S-backed infrastructure with Cyfuture AI, organizations can accelerate AI applications, improve graphics rendering, and scale virtual workstations with minimal effort.

Frequently Asked Questions (FAQs)

• What is the memory capacity of NVIDIA L40S?
  

  The L40S comes with 48 GB GDDR6 memory with ECC support.

• Can L40S handle large AI models?
  

  Yes, its memory and tensor core performance make it suitable for generative AI, LLM inference, and AI-trained models.

• Does the L40S support virtualization?
  

  Yes, it supports vGPU, allowing multiple users or VMs to share GPU resources efficiently.

• How does L40S differ from H100 and A100?
  

  The L40S is designed for mixed workloads including AI inference and graphics, while H100 is optimized for large-scale AI training and A100 is for general-purpose AI workloads.

• Why choose Cyfuture AI for L40S GPU workloads?
  

  Cyfuture AI provides scalable, high-performance infrastructure using L40S GPUs for AI applications, generative AI, and graphics-intensive workloads.