Quick Answer
NVIDIA L4 is the better choice for power-efficient AI inference, video processing and high-density deployments, offering 24GB memory at 72W. NVIDIA L40S provides 48GB memory, higher bandwidth and substantially more compute for generative AI, fine-tuning, rendering and heavier workloads. Choose based on memory, throughput, power, deployment needs and budget constraints.
One choice can shape the cost, density, and performance of your AI infrastructure: NVIDIA L4 or NVIDIA L40S. Both GPUs are based on the NVIDIA Ada Lovelace architecture, but they address different deployment priorities.
- NVIDIA L4 focuses on efficient video processing, AI inference, graphics, and virtual workstation workloads. Its low-profile, single-slot design makes it suitable for servers where power, cooling and deployment density matter.
- NVIDIA L40S offers substantially more compute, memory capacity and memory bandwidth for generative AI, model fine-tuning, rendering and other demanding mixed workloads.
The question is not simply which GPU is faster. It is which accelerator fits your workload, model size, latency target, power budget, and scaling plan.
Where the NVIDIA L4 Delivers the Most Value
The NVIDIA L4 24GB Tensor Core GPU is a budget-friendly, energy-aware accelerator for high throughput and low latency workloads across cloud, data center and edge environments.
- Performance and efficiency: L4 provides 30.3 FP32 TFLOPS, 24GB of GDDR6 ECC memory and 300GB/s of memory bandwidth within a 72W maximum power envelope.
- Video and compute acceleration: A single server can host about 1,040 concurrent AV1 streams at 720p30 using Tensor, RT and CUDA cores. This suits streaming stacks and inference pipelines.
- Deployment flexibility: PCIe 4.0 works out of the box and remains backward compatible with PCIe 3.0 for older hosts and storage stacks. Multi-GPU inferencing scales cleanly across racks.
- Efficiency and cost: Up to 99% better energy efficiency than CPU-only designs reduces rack space and operating expense. L4 is a strong fit where low power draw is a priority.
Where the NVIDIA L40S Delivers the Most Value
The NVIDIA L40S targets workloads that need more memory, bandwidth and per-GPU performance than the L4 can provide. It is particularly relevant when teams want to consolidate generative AI, rendering, simulation, and virtual workstation workloads on a higher-capacity accelerator.
- Generative AI and model development: Fourth-generation Tensor Cores and Transformer Engine support FP8 and FP16 processing for AI training and inference. L40S is a strong fit for generative AI inference, multimodal pipelines, fine-tuning and smaller or single-node training workloads.
- Higher memory capacity: Its 48GB of GDDR6 ECC memory and 864GB/s of bandwidth provide more room for larger models, batches, scenes and datasets than L4.
- Virtualization: NVIDIA vGPU software allows supported virtual machines and virtual workstations to share GPU capacity. Because L40S does not support MIG, teams should test performance under expected contention rather than assume hardware-isolated performance for every VM.
- Rendering and visualization: Third-generation RT Cores, fourth-generation Tensor Cores and DLSS 3 frame-generation technology support real-time rendering, virtual production, Omniverse and professional visualization.
- Data center deployment: L40S is a passive, full-height, full-length, dual-slot PCIe Gen4 GPU with a 350W maximum power rating. Servers must provide the necessary slots, airflow, power, and thermal capacity.
For a broader look across NVIDIA’s inference lineup beyond L4 and L40S, see our best GPUs for AI inferencing guide.
NVIDIA L4 vs L40S Specs Compared
The specification differences between L4 and L40S explain why each GPU fits a different class of workload. The following comparison highlights the factors that most directly affect model capacity, processing speed, deployment density, and infrastructure requirements.
| Specification | NVIDIA L4 | NVIDIA L40S |
|---|---|---|
| Architecture | Ada Lovelace | Ada Lovelace |
| GPU memory | 24GB GDDR6 ECC | 48GB GDDR6 ECC |
| Memory bandwidth | 300GB/s | 864GB/s |
| FP32 performance | 30.3 TFLOPS | 91.6 TFLOPS |
| FP16 Tensor cores | 242 TFLOPS | 362.05 I 733 TFLOPS |
| FP8 Tensor cores | 485 TOPs | 733 I 1,466 TFLOPS |
| PCIe interface | PCIe Gen4 x16, 64GB/s | PCIe Gen4 x16, 64GB/s bidirectional |
| Maximum power | 72W | 350W |
| Form factor | 1-slot low-profile, PCIe | 4.4″ (H) x 10.5″ (L), dual slot |
Use Cases: NVIDIA L4 vs L40S
The better GPU depends on how an application uses memory, Tensor Cores, graphics resources, and video engines. Comparing both GPUs across practical workloads provides a clearer decision than relying on peak performance figures alone.
Machine Learning and Model Inference
NVIDIA L4 suits chatbots, recommendation engines, fraud scoring, computer vision and language-model inference where throughput per watt and deployment density matter. Its 24GB memory capacity is appropriate for models and batches that fit comfortably within available VRAM.
NVIDIA L40S offers more headroom for larger models, higher batch sizes, multimodal pipelines and fine-tuning. Its 48GB memory and higher Tensor performance can improve throughput or reduce completion time when the workload effectively uses its compute resources.
For large-scale pretraining or tightly coupled multi-GPU training, teams should also evaluate higher-memory platforms with high-bandwidth GPU-to-GPU interconnects. This recommendation follows from the memory and interconnect limitations of L4 and L40S rather than an inability to run training workloads on either GPU.
For a full breakdown of where L4 and L40S sit relative to A100, H100 and H200 on memory, bandwidth and interconnect, see our NVIDIA H200 vs H100 vs A100 vs L40S vs L4 comparison.
Gaming and Graphics Rendering
NVIDIA L4 can support cloud gaming, lightweight AR or VR, virtual workstations and graphics workloads where session density and power efficiency matter. Its RT and Tensor Cores accelerate ray tracing, denoising, and AI-assisted graphics.
NVIDIA L40S is better suited to demanding VFX, animation, real-time 3D, virtual production and Omniverse workflows. Its higher FP32 and RT Core performance provides more per-GPU rendering headroom for complex scenes and interactive visualization.
See our best GPUs for rendering and video editing for a deeper comparison across the full rendering-focused GPU lineup, including where L40S ranks against other options.
Cloud Infrastructure
NVIDIA L4 fits cloud-native inference, video analytics, image recognition and virtual desktop environments. Its 72W, low-profile design can simplify deployment in servers with limited power and space, although actual density depends on the server platform and airflow design.
NVIDIA L40S suits cloud environments that consolidate AI, rendering and virtual workstation workloads on fewer, more powerful GPUs. Both GPUs support NVIDIA vGPU, but they use time-sliced sharing instead of MIG-backed hardware partitioning.
Generative Workloads
NVIDIA L4 can run image generation and smaller language-model inference while prioritizing energy efficiency. NVIDIA reports up to 2.5 times the Stable Diffusion v2.1 image-generation performance of the previous-generation T4 under its published test configuration.
NVIDIA L40S provides more memory bandwidth, VRAM and Tensor performance for generative AI inference, multimodal models, diffusion pipelines and fine-tuning.
Actual token throughput, latency and supported context length depend on model size, quantization, batch size, KV-cache requirements and the serving framework. Therefore, a larger theoretical Tensor figure does not guarantee the same performance improvement in every generative AI workload.
Video and Content
NVIDIA L4 is particularly suited to high-density video transcoding and AI video pipelines. NVIDIAโs 1,040-stream result applies to an eight-GPU L4 server running AV1 at 720p30 with the low-latency P1 preset, not to a single GPU.
NVIDIA L40S includes three NVENC and three NVDEC engines with AV1 encode, and decode support. Its additional compute headroom can support video pipelines that also require rendering, effects, upscaling, captioning or AI inference.
Production capacity should be benchmarked using the required codec, resolution, bitrate, encoding preset, and preprocessing stages.
Source: L40S GPU for AI and Graphics Performance | NVIDIA
HPC and Simulation
NVIDIA L4 can support AI-assisted preprocessing, postprocessing, visualization, and smaller parallel workloads where power efficiency matters.
NVIDIA L40S is the stronger option for FP32-oriented computer-aided engineering, visualization, digital twins and Omniverse-based simulation workflows.
However, solver performance varies considerably across applications. CFD and scientific workloads should be tested using the actual solver, mesh size, numerical precision, and dataset before selecting either GPU. NVIDIA also positions both products across selected simulation workloads, but that does not make either GPU the universal choice for all HPC applications.
How to Compare L4 and L40S on Price and Performance
GPU pricing should be evaluated against the useful output a workload produces rather than the hourly or monthly rate alone. Throughput, latency, memory utilization, energy consumption, and job completion time provide a more meaningful basis for comparison.
| Priority | Better starting point | Why |
|---|---|---|
| Cost per inference | L4 | Low power and deployment density can improve efficiency for always-on inference |
| Time to results for fine-tuning | L40S | More memory, bandwidth and Tensor performance provide greater per-GPU headroom |
| Video-transcoding density | L4 | Strong video-engine capability within a 72W power envelope |
| Mixed AI and rendering workloads | L40S | Higher AI, FP32 and RT performance supports workload consolidation |
| Power and cooling constraints | L4 | 72W maximum power compared with 350W for L40S |
| Models requiring more than 24GB VRAM | L40S | 48GB memory capacity |
Note: These recommendations are starting points rather than universal performance guarantees. Compare cost per successful output, latency, throughput, GPU memory consumption and power usage with the intended production workload.
Find the Right NVIDIA GPU for Your Workload with AceCloud
NVIDIA L4 and L40S serve different infrastructure priorities. Choose L4 for power-efficient AI inference, video transcoding, virtual workstations, and high-density deployments. Choose L40S when your workloads need more VRAM, memory bandwidth and compute for generative AI, fine-tuning, rendering or engineering visualization.
The best choice depends on how your model uses memory, precision, batch size, video engines, and available power. Comparing only TFLOPS or hourly pricing can lead to an inefficient deployment.
AceCloud helps you test NVIDIA L4 and L40S GPUs using your actual models and production settings before you scale. Measure throughput, latency, memory usage, and cost per output in a flexible cloud environment.
Book a free consultation with AceCloud to select the right GPU and configuration for your workload.
Give us a call at+91-789-789-0752to connect with our experts and start building on NVIDIA L4 or NVIDIA L40S on cloud today.Microsoft Clarity code & free credits section
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๐ Claim Free CreditsFrequently Asked Questions
L4 suits smaller or quantized models where power efficiency and deployment density matter. L40S is better for larger models, longer contexts, higher batch sizes, and greater per-GPU throughput.
Yes. L40S supports LLM fine-tuning and smaller training workloads with 48GB of memory. Large-scale pretraining may require GPUs with more memory and faster GPU-to-GPU interconnects.
Yes. L40S provides substantially higher Tensor, FP32 and graphics performance, along with nearly three times the memory bandwidth. Actual gains depend on the model, precision and software stack.
L4 is generally better for high-density, power-efficient video transcoding, and streaming. L40S is more suitable when video processing is combined with demanding AI, rendering, or graphics workloads.
No. Neither L4 nor L40S supports NVIDIA Multi-Instance GPU. Virtualized resource sharing instead depends on supported NVIDIA vGPU profiles and platform configuration.
No. Both GPUs use PCIe Gen4 x16 and do not support NVLink. Multi-GPU scaling therefore depends on PCIe topology, networking and workload partitioning.
NVIDIA L4 consumes considerably less power, with a maximum power rating of 72W. L40S can consume up to 350W but delivers much higher compute and graphics performance.
Yes. L40S supports NVIDIA vGPU and is well suited to professional visualization, 3D rendering, digital twins, Omniverse and graphics-intensive virtual workstations.
VRAM requirements depend on model size, precision, context length, batch size, and concurrent users. L4 provides 24GB for efficient inference, while L40S offers 48GB for larger or more demanding workloads.
