Use-case guide · Information extraction
Should you pick RTX 6000 Ada for information extraction?
RTX 6000 Ada 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 6000 Ada 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 6000 Ada is on the GPU detail page — click through for the sortable list.
Throughput
On information extraction workloads, RTX 6000 Ada 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.