LLM VRAM calculator
Parameters × quantization → VRAM, with a live match against consumer and datacenter GPUs.
Every number here is computed in your browser from disclosed formulas and vendor-published specs — no APIs, no accounts, no black boxes. Just the conversions AI practitioners actually need.
GPU specs shared across every tool — tables can never disagree.
Every tool runs entirely client-side
Parameters × quantization → VRAM, with a live match against consumer and datacenter GPUs.
Decode speed ≈ memory bandwidth × 0.6 ÷ model size. Compare every GPU.
6 × params × tokens ÷ achieved FLOPS → GPU-hours for your pretrain or fine-tune.
Input × price + output × price — one workload, every major provider compared.
Total VRAM ÷ card memory → how many GPUs your rig needs.
Hourly × cards × hours — the same job priced across RunPod, Lambda, AWS, and Azure.