AI Model Stack Builder

Add AI models from 6 categories to build your stack. Each model's VRAM is calculated based on its parameters and configuration. Models marked 'Always On' are loaded simultaneously, while 'On Demand' models only add the largest one to peak VRAM. Total includes 0.5 GB system overhead.

The AI landscape has shifted from single-model deployments to complex multi-model pipelines. Modern AI applications routinely combine a large language model for reasoning, a speech-to-text model for audio input, a text-to-speech model for audio output, an image generation model for visual content, and an embedding model for retrieval-augmented generation. Each model competes for the same finite VRAM, and exceeding capacity means either the stack fails to load or models must be swapped in and out with significant latency penalties. GPU hardware represents one of the largest capital expenditures in AI development. An NVIDIA A100 80GB costs $15,000-$20,000, and even consumer GPUs like the RTX 4090 (24 GB) run $1,600-$2,000. Making the wrong hardware purchase because you did not accurately estimate your stack's total VRAM requirement is an expensive mistake that can set back a project by weeks while you wait for replacement hardware. Cloud GPU costs compound this — renting an 80 GB A100 costs $2-4/hour, so overprovisioning wastes money while underprovisioning causes OOM crashes. This calculator solves the multi-model planning problem that single-model VRAM calculators cannot. By letting you build your complete stack — with both always-on and on-demand models — it reveals whether your hardware can handle your intended pipeline before you commit to purchases or cloud contracts.

Scenario 1: The Local Voice Assistant — A developer building a privacy-first voice assistant needs Whisper Medium (1.5 GB) for speech recognition, a Q4 Llama 3 8B (5 GB) for conversation, and Piper TTS (0.3 GB) for speech synthesis. With 0.5 GB overhead, the total is 7.3 GB — fitting comfortably on a 12 GB RTX 4070. Without this calculator, the developer was considering a 24 GB GPU, saving $600 by knowing 12 GB is sufficient. Scenario 2: The Creative Studio Setup — A freelance artist wants Stable Diffusion XL (6.5 GB at FP16), a Q4 Mistral 7B (4 GB) for prompt refinement, and CLIP ViT-L (1.5 GB) for image understanding, all on an RTX 4090 (24 GB). The calculator shows 12.5 GB peak usage — plenty of room. They add a video generation model (Mochi, 10 GB) as on-demand, bringing peak to 22.5 GB — tight but feasible. Scenario 3: The RAG Pipeline — An enterprise team deploying a retrieval-augmented generation system needs an embedding model (BGE Large, 0.7 GB) running always-on for document indexing, plus a Q8 Llama 3 70B (70 GB) for generation. The calculator immediately shows they need at least an A100 80GB or dual RTX 4090s. This analysis saved weeks of debugging OOM errors on inadequate hardware.

1. Forgetting CUDA context overhead — Each model loaded on a GPU consumes 200-500 MB of CUDA context memory beyond its weights. Loading 5 models can add 1-2.5 GB of overhead that is invisible in model-card specifications. This calculator includes per-model overhead in the total. 2. Confusing always-on and on-demand VRAM calculations — Some users add up all model sizes for peak VRAM. In practice, on-demand models are only loaded when needed. Peak VRAM equals all always-on models plus the single largest on-demand model — not all on-demand models simultaneously. 3. Ignoring quantization opportunities — Many users plan their stacks using FP16 (full precision) for all models. Applying Q4 quantization to LLMs reduces their VRAM by approximately 75% with only modest quality loss. A 70B parameter model drops from ~140 GB to ~35 GB at Q4 — the difference between needing an H100 and fitting on an RTX 4090. 4. Not accounting for batch size and concurrent requests — The VRAM calculations in this tool assume single-request inference. If your application serves multiple concurrent users, KV cache memory scales linearly with batch size. Serving 4 concurrent requests to a 7B LLM with 4K context can add 2-4 GB of additional VRAM requirement. 5. Overlooking system memory requirements — GPU VRAM is not the only bottleneck. The CPU must hold model weights during loading, intermediate tensors, and the inference runtime. Ensure your system has at least 2x the total model size in system RAM, or loading will fail or thrash to swap.