NVIDIA L40S vs A100: Which Should You Choose?

The NVIDIA L40S has emerged as a compelling alternative to the A100 in 2026. Based on the Ada Lovelace architecture, the L40S offers 48GB of GDDR6X memory and strong inference performance at a lower price point. But how does it really compare to the battle-tested A100? This guide breaks it down.

Specifications Head-to-Head

| Spec | L40S 48GB | A100 80GB SXM | A100 40GB PCIe |

|------|-----------|---------------|-----------------|

| Architecture | Ada Lovelace | Ampere | Ampere |

| VRAM | 48GB GDDR6X | 80GB HBM2e | 40GB HBM2e |

| Memory Bandwidth | 864 GB/s | 2,039 GB/s | 1,555 GB/s |

| FP16 Tensor | 362 TFLOPS | 312 TFLOPS | 312 TFLOPS |

| FP8 Tensor | 724 TFLOPS | N/A | N/A |

| TDP | 350W | 400W | 250W |

| NVLink | No | Yes (600 GB/s) | No (PCIe only) |

| Cloud Price | $1.09-1.49/hr | $1.69-2.49/hr | $1.09-1.29/hr |

Performance Benchmarks

LLM Training (LLaMA 3 8B, Full Fine-Tune)

| Metric | L40S 48GB | A100 80GB |

|--------|-----------|-----------|

| Tokens/second | 5,200 | 5,800 |

| Time for 1 epoch (1B tokens) | 53.4 hrs | 47.9 hrs |

| Cost at RunPod rate | $65.14 | $90.52 |

| **Cost efficiency** | **$12.52/B tokens** | **$18.90/B tokens** |

LLM Inference (LLaMA 3 8B, vLLM, FP16, batch 16)

| Metric | L40S 48GB | A100 80GB |

|--------|-----------|-----------|

| Throughput (tok/s) | 1,400 | 1,600 |

| First token latency | 25ms | 32ms |

| Cost per 1M tokens | $0.18 | $0.33 |

Stable Diffusion XL (1024x1024)

| Metric | L40S 48GB | A100 80GB |

|--------|-----------|-----------|

| Images/min | 30 | 48 |

| Cost per 1K images | $0.56 | $1.26 |

Where the L40S Wins

Cost efficiency for inference:: 40-50% cheaper per token than A100

FP8 support:: Native FP8 tensor cores for quantized models

Price point:: $1.09-1.49/hr vs $1.69-2.49/hr

Lower power draw:: 350W vs 400W

Better for quantized models:: FP8 and INT8 performance is excellent

Where the A100 Wins

VRAM capacity:: 80GB vs 48GB -- critical for large models

Memory bandwidth:: 2.4x higher HBM2e bandwidth

Multi-GPU scaling:: NVLink support (SXM variant)

Training throughput:: Faster for memory-bandwidth-bound workloads

Ecosystem maturity:: Better framework optimization

Use Case Recommendations

Choose L40S When:

Choose A100 When:

Provider Pricing (March 2026)

L40S 48GB

| Provider | On-Demand | Spot |

|----------|----------|------|

| RunPod | $1.24/hr | $0.74/hr |

| Lambda Labs | $1.29/hr | $0.89/hr |

| Vast.ai | $1.09/hr | $0.59/hr |

A100 80GB SXM

| Provider | On-Demand | Spot |

|----------|----------|------|

| Vast.ai | $1.69/hr | $0.89/hr |

| RunPod | $1.89/hr | $1.09/hr |

| Lambda Labs | $1.99/hr | $1.29/hr |

Our Verdict

The **L40S is the new sweet spot for inference** in 2026. It delivers 80-90% of the A100's performance at 60% of the cost. For training, the **A100 80GB remains superior** due to higher memory bandwidth and larger VRAM. If you primarily run inference and your models fit in 48GB, the L40S saves you significant money.

Compare L40S and A100 prices --> →