Quick Answer
NVIDIA L4 suits power-efficient inference, video, and high-density deployments with 24GB VRAM at 72W. L40S targets heavier generative AI, fine-tuning, and rendering with 48GB VRAM and higher bandwidth. RTX PRO 4500 Blackwell sits between them with 32GB GDDR7, 800GB/s bandwidth, FP4, PCIe Gen5, and MIG support at 165W. Choose based on workload, latency, power, and budget.
Serving a chatbot to thousands of users is a different job from fine-tuning a model or generating images, and it calls for a different GPU. That’s the real gap between NVIDIA L4 and L40S. Both run on Ada Lovelace, but each is tuned for a different kind of AI work.
- 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.
- RTX PRO 4500 Blackwell adds another option for teams whose workloads have moved beyond the practical limits of a 24GB card but do not necessarily require the L40S’s 48GB capacity or 350W power envelope. Its 32GB memory, Blackwell Tensor Cores and support for FP4 make it particularly relevant to newer inference stacks.
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 Gives You the Best Return
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: NVIDIA reports up to approximately 1,040 concurrent AV1 streams at 720p30 for an eight-GPU L4 server under its published configuration. This makes L4 particularly relevant to dense streaming, transcoding and video-AI 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.
For inference specifically, L4 makes the most sense when the model, KV cache and serving overhead fit comfortably within 24GB. Embedding models, recommendation systems, computer vision, video analytics and smaller or quantized LLMs are natural starting points.
Where the NVIDIA L40S Gives You the Best Return
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 maximum power consumption of 350W.
The biggest reason to move from L4 to L40S is not simply higher theoretical performance. Its 48GB memory capacity changes the size of model, KV cache, batch or working dataset that can remain resident on one GPU. That becomes increasingly important for larger language models, long-context serving, higher concurrency and multimodal workloads.
For a broader look across NVIDIA’s inference lineup beyond L4 and L40S, see our best GPUs for AI inferencing guide.
Where the RTX PRO 4500 Gives You the Best Return
The NVIDIA RTX PRO 4500 Blackwell Server Edition targets inference and mixed AI workloads that need more memory and bandwidth than L4 while maintaining a considerably lower power envelope than L40S. Its 32GB capacity also creates a useful middle ground between the two GPUs.
- Blackwell inference capabilities: RTX PRO 4500 supports FP4, FP8, FP16 and BF16 precision. FP4 gives compatible inference stacks another way to reduce model memory requirements and increase Tensor Core throughput when the model, framework and accuracy requirements support lower-precision execution.
- Memory and bandwidth: Its 32GB of GDDR7 ECC memory and 800GB/s of memory bandwidth provide more capacity and substantially more bandwidth than L4’s 24GB and 300GB/s, while remaining close to L40S’s 864GB/s bandwidth.
- Performance and efficiency: RTX PRO 4500 delivers 51 FP32 TFLOPS while operating within a 165W maximum power envelope. This positions it between the 72W L4 and 350W L40S for teams balancing performance, power consumption and deployment density.
- MIG-based workload isolation: RTX PRO 4500 supports up to two 16GB MIG instances, allowing compatible workloads to use hardware-isolated GPU partitions. This can be valuable for multi-tenant inference, separate AI services and environments where predictable GPU resource allocation matters.
- Data center deployment: PCIe Gen5 x16 provides a newer host interface, while its single-slot, full-height form factor can help teams deploy more GPU capacity without the dual-slot footprint required by L40S.
NVIDIA L4 vs RTX PRO 4500: Which is Better for AI Inference?
Choose NVIDIA L4 when your inference workload fits comfortably within 24GB and power efficiency or deployment density is the priority. RTX PRO 4500 Blackwell is the better step up when you need more memory and bandwidth, with 32GB of GDDR7 and 800GB/s compared with L4’s 24GB and 300GB/s. It also adds FP4 and MIG support for newer inference and multi-tenant workloads. The trade-off is power consumption: RTX PRO 4500 is rated at 165W, while L4 operates at 72W. NVIDIA also reports more than 5x L4 performance for small-model AI inference, although real-world results will vary by model, precision, batch size, and serving stack.
NVIDIA L4 vs L40S vs RTX PRO 4500: Specs Compared
The specification differences between these GPUs explain why each fits a different class of workload. Memory capacity tells you what can fit. Memory bandwidth affects how quickly data can move. Tensor capabilities matter when your serving framework can use the supported precision.
| Specification | NVIDIA L4 | NVIDIA L40S | RTX PRO 4500 Blackwell Server Edition |
|---|---|---|---|
| Architecture | Ada Lovelace | Ada Lovelace | Blackwell |
| GPU memory | 24GB GDDR6 | 48GB GDDR6 ECC | 32GB GDDR7 |
| Memory bandwidth | 300GB/s | 864GB/s | 800GB/s |
| FP32 performance | 30.3 TFLOPS | 91.6 TFLOPS | 51 TFLOPS |
| FP16 Tensor performance | 242 TFLOPS* | 733 TFLOPS* | 406 TFLOPS |
| FP8 Tensor performance | 485 TFLOPS* | 1,466 TFLOPS* | 811 TFLOPS |
| FP4 Tensor performance | Not supported | Not supported | 1.6 PFLOPS |
| PCIe interface | PCIe Gen4 x16 | PCIe Gen4 x16 | PCIe Gen5 x16 |
| Maximum power | 72W | 350W | 165W |
| MIG support | No | No | Up to 2 × 16GB |
| Form factor | Single-slot, low-profile | Dual-slot, full-height/full-length | Single-slot, full-height/full-length |
*NVIDIA lists applicable Ada Tensor figures with sparsity. Peak specification-sheet figures should not be treated as direct predictions of application performance.
The important point is that the three GPUs are built around different trade-offs.
- L4 prioritizes efficiency and density.
- L40S maximizes memory capacity and per-GPU compute among these three.
- RTX PRO 4500 combines 32GB memory, high bandwidth, Blackwell precision support and MIG within a considerably lower power envelope than L40S.
Use Cases: NVIDIA L4 vs L40S vs RTX PRO 4500
The better GPU depends on how an application uses memory, Tensor Cores, graphics resources, video engines and supported precision. Comparing practical workloads provides a more useful answer than simply choosing the GPU with the largest headline performance number.
AI, Machine Learning and Model Inference
- NVIDIA L4 suits AI inference workloads such as chatbots, recommendation engines, fraud scoring, computer vision, speech processing and smaller language models 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 language and multimodal models, higher batch sizes, generative AI pipelines, fine-tuning and more compute-intensive AI workloads. Its 48GB memory and higher Tensor performance can improve throughput or reduce completion time when the workload effectively uses its additional compute resources.
- RTX PRO 4500 Blackwell Server Edition becomes relevant when the L4’s 24GB memory is insufficient or its memory bandwidth becomes a bottleneck, while the workload still fits within 32GB. Its 165W single-slot design combines 800 GB/s of memory bandwidth, roughly 2.7x that of the L4, with native FP4 support for supported quantized inference and up to two isolated 16GB MIG instances. This makes it worth evaluating for small- to medium-sized LLM and multimodal inference, agentic AI, RAG, vision AI, speech workloads, bandwidth-sensitive serving and multi-tenant deployments.
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.
- RTX PRO 4500 also targets mixed AI and professional visualization workloads. Its Blackwell architecture, fourth-generation RT Cores, 32GB GDDR7 memory and 165W single-slot design make it an additional option where teams need both AI acceleration and graphics capability without moving to the power profile of L40S.
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.
- RTX PRO 4500 introduces a third infrastructure model. Its 165W power envelope and single-slot design provide more compute and memory bandwidth than L4, while MIG support can create isolated GPU instances for compatible workloads.
L4 and L40S rely on supported vGPU or other software-level sharing approaches rather than MIG-backed hardware partitioning.
Generative AI 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.
- RTX PRO 4500 is particularly relevant to inference workloads that can exploit Blackwell’s lower-precision capabilities. Its 32GB memory also provides additional headroom over L4 for models, KV cache or multimodal components that push beyond 24GB.
Actual token throughput, latency and supported context length still depend on model size, quantization, batch size, KV-cache requirements and the serving framework. A larger theoretical Tensor number does not guarantee the same performance improvement in every production workload.
Video and Content
- NVIDIA L4 is particularly suited to high-density video transcoding and AI video pipelines. NVIDIA’s 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.
- RTX PRO 4500 also includes three NVENC and three NVDEC engines. Its newer Blackwell media engines add support relevant to modern professional video pipelines, making it another option for environments combining video processing with AI inference.
Production capacity should be benchmarked using the required codec, resolution, bitrate, encoding preset, and preprocessing stages.
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.
- RTX PRO 4500 may also suit mixed AI, visualization and data-processing workloads where Blackwell features and power efficiency are more important than maximizing 48GB single-GPU capacity.
Solver performance varies considerably across applications. CFD and scientific workloads should therefore be tested using the actual solver, mesh size, numerical precision and dataset before selecting a GPU.
How to Compare L4, L40S and RTX PRO 4500 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 |
|---|---|---|
| Power-efficient inference | L4 | 72W power envelope and compact form factor |
| High-density inference | L4 | Strong fit when workloads stay comfortably within 24GB |
| Models exceeding practical 24GB capacity | RTX PRO 4500 or L40S | 32GB and 48GB respectively provide additional memory headroom |
| Models requiring 40GB+ VRAM | L40S | 48GB gives it the clear capacity advantage |
| FP4 inference | RTX PRO 4500 | Only Blackwell GPU among these three with FP4 support |
| MIG-backed isolation | RTX PRO 4500 | Supports hardware-isolated MIG instances |
| Fine-tuning and heavier single-GPU workloads | L40S | 48GB memory and higher Tensor throughput |
| Mixed AI and rendering | L40S or RTX PRO 4500 | Both provide significantly more headroom than L4 |
| Video-transcoding density | L4 | Strong video capability within 72W |
| Power-conscious step up from L4 | RTX PRO 4500 | 165W versus 350W for L40S |
| Maximum VRAM among these three | L40S | 48GB |
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.
Five Questions to Ask Before Choosing
- Does the complete workload fit comfortably within 24GB?If yes, start by evaluating L4. Do not look only at the model weights. Include KV cache, activations, runtime overhead, batches and concurrent requests when estimating actual memory requirements.
- Does the workload need more than 32GB on one GPU?If yes, L40S becomes the clear choice among these three from a capacity perspective because it provides 48GB.
- Can Your Inference Stack Use FP4 Effectively?If the model, framework and acceptable accuracy level support FP4, RTX PRO 4500 deserves closer evaluation.FP4 can improve memory efficiency and Tensor throughput, but only when the software stack actually exploits it.
- Are Power and Rack Density Major Constraints?Start with L4 and RTX PRO 4500. Their maximum power figures are 72W and 165W respectively, compared with 350W for L40S.
- Do You Need Multiple Isolated Workloads on One GPU?RTX PRO 4500 has an architectural advantage here because it supports MIG.That can matter for platforms serving multiple teams, tenants or inference services where resource isolation and predictable allocation are important.
Benchmark the Workload, Not the Specification Sheet
If two GPUs still appear suitable after this comparison, run the model you actually intend to serve. Test using the expected:
- model and quantization
- input and output lengths
- batch size
- concurrent users
- precision
- serving framework
- latency target
Measure time to first token, inter-token latency, tokens per second, requests per second, GPU memory utilization and cost per useful output.
For image or video workloads, replace token metrics with outputs per minute, render time, latency and cost per completed asset. This approach is more useful than assuming a GPU with twice the theoretical compute will produce twice the application performance.
Practical Example: Production LLM Inference
Consider a customer-support application serving an LLM to multiple simultaneous users. As request volume, context length and concurrency increase, the GPU must maintain acceptable latency without creating unnecessary infrastructure cost.
| Production requirement | GPU to evaluate first | Reason |
|---|---|---|
| Model and runtime fit comfortably within 24GB with high request volume | NVIDIA L4 | Efficient 24GB inference within a 72W power envelope |
| Workload exceeds practical 24GB capacity but fits within 32GB | RTX PRO 4500 | Additional memory, 800GB/s bandwidth and 165W power |
| Stack can exploit FP4 | RTX PRO 4500 | Blackwell FP4 capability |
| Multiple isolated GPU workloads are required | RTX PRO 4500 | MIG support |
| Larger model, long context or high concurrency pushes beyond 32GB | NVIDIA L40S | 48GB provides greater single-GPU memory headroom |
| Maximum per-GPU capacity among these options matters most | NVIDIA L40S | Highest VRAM and memory bandwidth in the comparison |
The takeaway is that choosing an inference GPU is less about selecting the largest specification and more about matching hardware to workload behavior. Model size, concurrency, precision, latency requirements, bandwidth and power constraints all matter.
Find the Right NVIDIA GPU for Your Workload with AceCloud
NVIDIA L4, L40S and RTX PRO 4500 solve different infrastructure problems.
- Choose L4 when power-efficient inference, video processing and high deployment density matter and your workload fits comfortably within 24GB.
- Choose L40S when you need 48GB of VRAM, higher per-GPU compute and more headroom for larger generative AI, fine-tuning, rendering or engineering workloads.
- Consider RTX PRO 4500 Blackwell when 32GB is sufficient and your deployment benefits from FP4, MIG, PCIe Gen5, high memory bandwidth or a substantially lower power envelope than L40S.
The best choice still depends on your actual model, precision, batch size, context length, concurrency and performance targets.
AceCloud lets you evaluate these GPUs using your actual production settings before you scale. Measure throughput, latency, GPU memory usage and cost per useful output instead of committing based only on headline specifications.
Book a free consultation with AceCloud to identify the GPU and infrastructure configuration that fits your workload.
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🎁 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.
L40 is the earlier Ada Lovelace workstation GPU, built mainly for rendering and visualization. L40S adds a Transformer Engine and stronger FP8 and FP16 Tensor performance for generative AI, and has largely replaced L40 in cloud deployments.
NVIDIA L40S delivers 91.6 TFLOPS of FP32 performance, compared with 30.3 TFLOPS on the NVIDIA L4.
NVIDIA L40S has 48GB of GDDR6 ECC memory, double the 24GB on NVIDIA L4.
