L40S 48GB vs RTX 5090 32GB for LLM inference

L40S 48GB vs RTX 5090 32GB: verdict

Choosing between an L40S 48GB and an RTX 5090 32GB for LLM inference comes down to three questions: how much VRAM do you actually need, how fast does each card move tokens once the model is loaded, and what does it cost to rent either one today. On raw capacity, L40S 48GB has the edge — 48GB on the L40S 48GB against 32GB on the RTX 5090 32GB, which matters directly for how large a model or how many concurrent users you can serve on a single card before reaching for tensor parallelism. On raw memory bandwidth, which is usually the real bottleneck for token-by-token decoding rather than compute, RTX 5090 32GB is ahead at 864 GB/s on the L40S 48GB versus 1792 GB/s on the RTX 5090 32GB. Live pricing wasn't fresh enough for a head-to-head comparison today, so we're showing catalog estimates instead, which is worth weighing against the raw specs if you're optimizing for cost per token rather than peak capability. Neither card is a strictly better choice in every dimension — the right pick depends on whether your deployment is VRAM-bound (a large model, long context, or many concurrent users) or throughput-bound (a smaller model where decode speed dominates unit economics). The spec table above breaks down the full comparison — VRAM, bandwidth, compute throughput at bf16 and fp8, NVLink support, and power draw — and the models table above shows which currently-popular models actually fit on each card.