Use-case guide · Information extraction

Should you pick RTX A6000 for information extraction?

RTX A6000 has 48 GB VRAM. Whether it's the right fit for information extraction depends on your model size, expected QPS, and budget. Below is what we're seeing in production.

VRAM + model fit

RTX A6000 fits models up to ~34B parameters in BF16 comfortably with room for KV-cache. For information extraction specifically, you'll want to leave headroom for context length growth.

Pricing

Live pricing across all providers for RTX A6000 is on the GPU detail page — click through for the sortable list.

Throughput

On information extraction workloads, RTX A6000 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.