Use-case guide · Vision-language

Should you pick A2 for vision-language?

A2 has 16 GB VRAM. Whether it's the right fit for vision-language depends on your model size, expected QPS, and budget. Below is what we're seeing in production.

VRAM + model fit

A2 fits models up to ~11B parameters in BF16 comfortably with room for KV-cache. For vision-language specifically, you'll want to leave headroom for context length growth.

Pricing

Live pricing across all providers for A2 is on the GPU detail page — click through for the sortable list.

Throughput

On vision-language workloads, A2 typically delivers the throughput published in its FP16 spec, minus the framework overhead (vLLM ≈ 85% MFU, TGI ≈ 70%).

Try the calculator to size the hardware for your specific model, or see all GPUs on the InferenceScore leaderboard.