NVIDIA · Q3 2023
L40S
NVL
The NVIDIA L40S NVL is a high-performance datacenter GPU designed for AI, machine learning, and high-performance computing workloads. It is part of the Ada Lovelace architecture, offering enhanced performance and efficiency over previous generations. Targeted at enterprise and cloud environments, it features advanced capabilities for large-scale AI model training and inference, making it a key component in modern AI infrastructure.
Provider Marketplace
All Cloud Providers
Compute Performance
Architecture
Memory & VRAM
Connectivity & Scaling
Virtualization
Power & Efficiency
Physical Design
Thermals & Cooling
Software Ecosystem
Server & Deployment
System Compatibility
Benchmarks & Throughput
Structured Sparsity
Supported (up to 2x vs dense)
Transformer Throughput
Supported (Transformer Engine)
Multi-GPU Scalability
Scaling Efficiency
Scaling Characteristics
Workload Readiness
LLM Training
The L40S NVL, based on the Ada Lovelace architecture, is suitable for training large language models up to 70B parameters in a single-node setup due to its high VRAM capacity. For 400B+ models, multi-node configurations are recommended.
LLM Inference
Highly efficient for inference tasks with excellent token-per-second throughput, thanks to 4th-gen Tensor cores and ample VRAM for KV cache management.
Vision Training
Optimized for vision training tasks with significant improvements in throughput and efficiency due to Ada Lovelace architecture and enhanced Tensor cores.
Diffusion Models
Well-suited for diffusion models, leveraging high VRAM and Tensor core capabilities to accelerate training and inference processes.
Multimodal AI
Capable of handling multimodal AI tasks efficiently, benefiting from the architecture's support for diverse data types and operations.
Reinforcement Learning
Effective for reinforcement learning workloads, offering fast computation and high throughput for complex simulations and model updates.
HPC / Simulation
Limited FP64 support; not ideal for HPC simulations requiring high double-precision performance, but can handle mixed-precision tasks efficiently.
Scientific Computing
Suitable for scientific computing tasks that can leverage mixed-precision calculations, but not optimal for those requiring extensive FP64 precision.
Edge Inference
Not ideal for edge inference due to higher power consumption and larger form factor, better suited for data center environments.
Real-Time Serving
Excellent for real-time AI serving, providing low latency and high throughput with advanced Tensor cores and Ada Lovelace architecture.
Fine-Tuning
Highly efficient for full fine-tuning tasks, leveraging high VRAM and advanced architecture to handle large model updates.
LoRA Efficiency
Efficient for LoRA fine-tuning, benefiting from lower VRAM requirements and optimized Tensor core performance.
Market Authority
Key Strengths
The L40S NVL excels in AI and machine learning workloads, particularly in training large neural networks and performing complex inference tasks. Its architecture provides significant performance improvements in FP16 and INT8 operations, making it ideal for deep learning applications. The GPU's high memory bandwidth and capacity also support data-intensive tasks, setting it apart from alternatives.
Limitations
While the L40S NVL offers exceptional performance, it comes with a high power consumption and cost, which may not be suitable for all budgets. Availability can be constrained due to high demand in AI and HPC sectors. Users should also consider the need for advanced cooling solutions to manage its thermal output effectively.
Also in the Lineup
Expert Insight
The L40S represents a strategic leap in AI compute. When comparing cloud providers, consider not just the hourly rate, but also the interconnect bandwidth (InfiniBand/NVLink) and regional availability which can significantly impact total cost of ownership for large-scale training.