Simple LLM VRAM calculator for model inference (www.bestgpusforai.com)

A new Simple LLM VRAM calculator (updated Aug 2025) estimates GPU memory needs for LLM inference by taking model parameter count and chosen numeric precision (FP32, BF16, FP16, FP8, FP6, FP4, INT8) and returning a “From” (weights only) and “To” (weights + activations, CUDA kernels, workspaces and fragmentation) memory range. The tool is architecture-agnostic, applies a practical 1.2× overhead heuristic, and gives quick, actionable ranges — e.g., a 7B FP16 model ≈14–16.8 GB, 13B ≈26–31.2 GB, 70B FP16 ≈140–168 GB (70B FP32 ≈280–336 GB), GPT‑3 175B FP16 ≈350 GB weights → ~420 GB with overhead, and 405B FP16 ≈810–972 GB. This calculator matters because it helps engineers and researchers plan deployments, avoid out-of-memory crashes, and choose precision/optimization strategies before provisioning hardware. It highlights how precision (FP32→FP16→INT8/FP8/FP4) drastically reduces weight storage, while activations, batch/sequence length, CUDA buffers and fragmentation still drive total VRAM needs. The summary also points to common mitigations—quantization, CPU offload, model parallelism, batching/sequence tuning, and memory-efficient attention (e.g., FlashAttention)—so teams can match models to single GPUs or multi‑GPU rigs like A100/RTX series more reliably.