NVIDIA A100 80GB vs NVIDIA L40S

Analysis

The NVIDIA A100 80GB and L40S sit in different corners of NVIDIA's datacenter lineup — the A100 is a 2020 HBM-based compute accelerator, the L40S a 2023 Ada Lovelace card built as much for graphics and video workloads as for AI. For local LLM inference, the practical question is whether the L40S's newer architecture and lower power draw make up for giving up 32GB of capacity and well over half the memory bandwidth.

The A100 leads on both axes that matter for LLM inference: 80GB versus the L40S's 48GB (+67%), and 2,039 GB/s versus 864 GB/s (+136%) thanks to HBM2e over GDDR6. That gap compounds on real models — this site's calculator fits Llama 3.3 70B at Q6_K (67.37 GB, 22.0 tok/s) on the A100, three rungs above the L40S's best fit of Q3_K_M (40.72 GB, 15.4 tok/s); even at that same Q3_K_M quantization, the A100 decodes 137% faster (36.5 tok/s) than the L40S manages at its own ceiling, tracking the two cards' 2.36x bandwidth gap almost exactly. Across the 85 models this site tracks, the A100 runs 59 natively versus the L40S's 51. The L40S's real advantages sit elsewhere: Ada Lovelace's FP8 Transformer Engine, which Ampere lacks; hardware video encode/decode for mixed multimedia pipelines; and a 300W power draw against the A100 SXM4's 400W.

Bottom line: For LLM inference specifically, the A100 80GB is the stronger card on every metric that determines what fits and how fast it runs — pick it when local models are the primary workload and 80GB of HBM headroom matters. The L40S makes more sense when the deployment is genuinely mixed-use: video transcoding or rendering alongside inference, a tighter power or thermal budget, or a workload where Ada Lovelace's newer FP8 path matters more than raw capacity. As a pure LLM box, the extra 32GB and 2.4x bandwidth on the A100 are hard to give up.