L4 24GB vs RTX 4090 24GB for LLM inference

L4 24GB vs RTX 4090 24GB: verdict

Choosing between an L4 24GB and an RTX 4090 24GB 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, both cards carry the same 24GB of VRAM — the difference between them lies in bandwidth, compute throughput, and price, not in how many concurrent users or what model size you can fit on a single card. On raw memory bandwidth, which is usually the real bottleneck for token-by-token decoding rather than compute, RTX 4090 24GB is ahead at 300 GB/s on the L4 24GB versus 1008 GB/s on the RTX 4090 24GB. 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.