GPU × use-case guide · Vision-language

Is the L40S a good GPU for vision-language?

The L40S is a ada NVIDIA GPU with 48 GB GDDR6 (864 GB/s), 362 TFLOPS BF16 / 733 TFLOPS FP8, and a 350 W TDP. Vision-language workloads care most about extra VRAM for the vision encoder and image tokens on top of the language model. Here's how the L40S measures up.

What models fit on a single L40S?

Weights only, reserving ~25% of the 48 GB for KV cache, activations and fragmentation. ✓ = fits on one card.

Largest single-card fit: Qwen 2.5 14B at BF16, Gemma 2 27B at FP8, Qwen 2.5 72B at INT4. Bigger models need tensor-parallel across 8 cards.

L40S for vision-language, specifically

Vision-language is context-heavy, so the KV cache — not the weights — is what fills the 48 GB. On the L40S you'll trade context length against batch size: long prompts mean fewer concurrent requests. Because it runs offline, batch aggressively to push tokens-per-dollar down. Size it precisely on the calculator.

L40S pricing across providers

Verdict

At 48 GB, the L40S is a solid mid-to-high-tier choice for vision-language: single-card up to Qwen 2.5 14B (BF16) or Gemma 2 27B (FP8), and cost-effective at ~$1.09/hr.