AI 모델 스택 빌더

This calculator aggregates VRAM requirements for multiple AI models running simultaneously, using the same per-model calculation methodology as our LLM VRAM Checker. For each model in the stack, VRAM is estimated based on parameter count, quantization level, and context length. The total VRAM calculation sums individual model requirements plus a shared overhead estimate for the inference framework and CUDA context. When models share the same GPU, there is typically 200-500MB of additional fixed overhead per model instance beyond the model weights. Model specifications are sourced from official model cards on Hugging Face and manufacturer documentation. GPU specifications come from NVIDIA, AMD, and Apple official data sheets. Categories covered: Large Language Models, Embedding Models, Image Generation (Stable Diffusion, DALL-E equivalents), Speech-to-Text (Whisper variants), Text-to-Speech, and Video Generation models. Limitations: Running multiple models simultaneously on a single GPU requires VRAM for all models to be loaded at once. Some inference frameworks support dynamic model loading/unloading, which would reduce peak VRAM usage but increase latency. Actual memory usage varies by framework, batch size, and concurrent request volume.