Use-case guide · Reasoning / agentic

Should you pick V100 32GB for reasoning / agentic?

V100 32GB has 32 GB VRAM. Whether it's the right fit for reasoning / agentic depends on your model size, expected QPS, and budget. Below is what we're seeing in production.

VRAM + model fit

V100 32GB fits models up to ~22B parameters in BF16 comfortably with room for KV-cache. For reasoning / agentic specifically, you'll want to leave headroom for context length growth.

Pricing

Live pricing across all providers for V100 32GB is on the GPU detail page — click through for the sortable list.

Throughput

On reasoning / agentic workloads, V100 32GB 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.