NVIDIA L4 vs L40S: Picking the Right Inference GPU for Your Data Center
When it comes to AI inference in the data center, raw TFLOPS isn’t the only metric that matters. Power consumption, server density, memory capacity, and total cost of ownership often determine which GPU delivers the best business outcome.
The NVIDIA L4 and L40S are both built on the Ada Lovelace architecture and optimized for inference — but they serve fundamentally different deployment scenarios. This comparison will help you choose the right one.
Specifications Side by Side
| Specification | L4 | L40S |
|---|---|---|
| Architecture | Ada Lovelace | Ada Lovelace |
| Memory | 24 GB GDDR6 | 48 GB GDDR6 ECC |
| Memory Bandwidth | 300 GB/s | 864 GB/s |
| FP8 Tensor | 485 TFLOPS | 1,466 TFLOPS |
| FP32 | 30.3 TFLOPS | 91.6 TFLOPS |
| RT Cores | Yes (3rd Gen) | Yes (3rd Gen) |
| TDP | 72W | 350W |
| External Power | None required | Required |
| Form Factor | Single-slot, low-profile | Dual-slot, full-height |
| PCIe | Gen4 x16 | Gen4 x16 |
The L4: Maximum Density, Minimum Power
The L4’s defining characteristic is its 72W power envelope in a single-slot, low-profile form factor. It requires no external power connector — it runs entirely from the PCIe slot. This means you can fit up to 8 L4 GPUs in a standard 1U or 2U server, creating an incredibly dense inference platform.
At 485 TFLOPS FP8, each L4 handles single-model inference workloads efficiently. The 24GB memory is sufficient for most production inference models, including medium-sized LLMs and all common computer vision models.
Cost per inference is lowest with the L4 when your models fit within 24GB and you’re optimizing for maximum GPU density per rack.
The L40S: Raw Performance for Complex Workloads
The L40S delivers 3x the raw FP8 performance of the L4, with 2x the memory and nearly 3x the memory bandwidth. It’s the right choice when your inference workloads demand more than a single L4 can provide — larger models, higher batch sizes, or multi-model pipelines.
The L40S also excels at converged workloads. With 212 RT TFLOPS and dedicated video encode/decode hardware, it handles AI inference, real-time rendering, and video processing simultaneously — making it ideal for cloud graphics, virtual workstation hosting, and AI-powered video platforms.
Decision Matrix
Choose the L4 if:
- Your inference models fit within 24GB of memory
- Maximum GPU density per server/rack is your priority
- Power efficiency (inference per watt) is critical
- You’re deploying inference at massive scale (thousands of GPUs)
- Your servers have limited power and cooling capacity
Choose the L40S if:
- Your models need more than 24GB of memory
- You need maximum inference throughput per GPU
- Your workloads mix AI inference with graphics or video
- You’re running large batch inference for offline processing
- vGPU or multi-tenant GPU sharing is part of your plan
The Math: 8x L4 vs 2x L40S
An interesting comparison: 8 L4 GPUs (576W total) deliver roughly 3,880 TFLOPS of FP8, while 2 L40S GPUs (700W total) deliver 2,932 TFLOPS. The 8x L4 configuration provides 32% more aggregate FP8 compute at 18% less power — but requires models that fit in 24GB per GPU.
If your models fit in the L4’s memory, the density and efficiency advantage is clear. If they don’t, the L40S is the only option.
The Bottom Line
The L4 and L40S aren’t competitors — they’re optimized for different ends of the inference spectrum. The L4 wins on efficiency and density; the L40S wins on flexibility and raw per-GPU throughput. Many data centers deploy both, using L4s for high-volume production inference and L40S for complex multi-modal or graphics-intensive workloads.
Need help designing your inference infrastructure? Contact us for server configurations, density planning, and TCO analysis.
