🧠 AI & Ollama Sizing

Local AI & LLM VRAM Sizer (2026 Edition)

⚡ Local LLM Memory Sizing Rule: Total VRAM consumption equals Model Weights plus KV Context Cache plus CUDA Runtime Overhead (~1.2GB). Running a 7B model at Q4_K_M requires approximately 5.8GB VRAM, while a 70B model requires at least 42GB VRAM. Never allocate 100% of physical VRAM to model weights—leave 2GB to 4GB for context cache to avoid severe CPU offloading stalls.

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Local AI & LLM VRAM Sizer (2026 Edition)

Calculate exact memory required for DeepSeek R1, LLaMA 3.3, and Qwen across quantization formats and context lengths.

1. AI Model Architecture

2. Quantization Precision

3. Context Window Size

4. Your Hardware Setup

How to Calculate VRAM for Local Inference (GGUF, Quantization & Context)

📊 Quantization Tradeoffs

Q4_K_M (4-bit Medium): The industry sweet spot. Retains 98.5% of full 16-bit model intelligence while reducing memory footprint by over 65%.
Q8_0 (8-bit Quantized): Near-lossless precision for complex coding and mathematical reasoning, requiring roughly double the VRAM of 4-bit weights.
FP16 / BF16 (Uncompressed): Full precision weights. Demands 2 bytes per parameter (e.g., 14GB for a 7B model, 140GB for a 70B model).

💡 Context Window Memory Math

4,096 Tokens (Standard): Adds roughly 500MB to 1GB to active GPU memory.
16,384 Tokens (Extended): Adds 2GB to 4GB of KV cache depending on attention head architecture (GQA vs MHA).
32,768+ Tokens (Long Context): Requires 6GB to 12GB of dedicated memory just for context memory tokens.