L40S x12 — Inference Deep Dive
The NVIDIA L40S is a PCIe-based GPU designed for AI inference, graphics, and enterprise workloads. Unlike H100/H200 SXM, it uses GDDR6 memory and connects via PCIe — making it more cost-effective for inference-heavy deployments where the extreme bandwidth of HBM is not the bottleneck.
System Snapshot
| Property |
Value |
| GPU |
NVIDIA L40S |
| Count |
12 |
| Memory per GPU |
48 GB GDDR6 |
| Total GPU Memory |
576 GB |
| Memory Bandwidth |
864 GB/s per GPU |
| FP8 Tensor Core TFLOPS |
~733 TFLOPS per GPU |
| BF16 / FP16 TFLOPS |
~366 TFLOPS per GPU (sparse) |
| TF32 TFLOPS |
~183 TFLOPS per GPU |
| GPU Interconnect |
PCIe 4.0 x16 (no NVLink) |
| Form Factor |
PCIe full-height, dual-slot |
| TDP |
350 W per GPU |
L40S vs H200: When to Choose L40S
| Factor |
L40S x12 |
H200 x8 |
| Total memory |
576 GB GDDR6 |
1,128 GB HBM3e |
| Memory bandwidth |
10.4 TB/s total |
38.4 TB/s total |
| GPU interconnect |
PCIe (no NVLink) |
NVLink 4.0 900 GB/s |
| Cost (approx) |
~$60K |
~$400K+ |
| Best for |
Cost-efficient inference |
Training + large model inference |
| Power/rack |
~4,200 W (12 GPUs) |
~5,600 W (8 GPUs) |
| Max single model |
~70B (multi-GPU) |
~405B (multi-GPU) |
Choose L40S when: inference throughput matters more than model size, cost is constrained, or you're running multiple smaller models simultaneously.
Topic Index
| # |
Topic |
Description |
| 01 |
Hardware Architecture |
Ada Lovelace die, GDDR6, PCIe topology, NVLink absence |
| 02 |
Inference Optimization |
vLLM, TRT-LLM, quantization, batching strategies |
| 03 |
Multi-GPU Strategy |
PCIe-constrained parallelism, pipeline vs tensor parallel |
| 04 |
Deployment Guide |
Multi-instance deployment, model sharding, production setup |
| 05 |
Benchmarks |
Throughput targets, latency baselines, cost/perf comparison |
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