“Can I Run It?” Stop guessing. Calculate exactly how much Video RAM you need to run models like Llama 3, Mixtral, and DeepSeek on your GPU.
Local LLM VRAM Calculator
Calculate VRAM requirements for Inference & Training. Supports Multi-GPU & CPU Offloading.
How to Estimate VRAM for Local AI Models
Running Large Language Models (LLMs) locally offers total privacy and zero API costs, but the hardware barrier is strict. The single most important factor is Video RAM (VRAM). If a model exceeds your GPU’s VRAM, it will overflow into your slower System RAM (DDR4/DDR5), causing generation speeds to plummet from 50 tokens/second to 2 tokens/second.
1. Model Size & Parameters
The “B” in model names (e.g., Llama-3-8B, Mistral-7B, Gemma-27B) stands for Billions of Parameters. This is the raw intelligence of the model.
Rule of Thumb: In 16-bit mode (FP16), you need roughly 2 GB of VRAM per 1 Billion parameters. However, almost nobody runs raw FP16 models locally.
2. Quantization (The Magic of “Q4”)
Quantization compresses the model weights. It reduces precision to save memory with negligible loss in intelligence.
- FP16 (16-bit): Uncompressed. requires massive VRAM. (Rarely used for inference).
- Q8 (8-bit): High fidelity, 50% smaller than FP16.
- Q4_K_M (4-bit): The “Gold Standard” for local LLMs. It retains ~98% of the model’s reasoning capability but requires only 0.7 – 0.8 GB per Billion parameters.
- Q2/Q3: Not recommended due to “brain damage” (incoherent outputs).
3. Context Window (KV Cache)
The “Context Window” is how much text the AI can remember in the current conversation. A larger context (e.g., 32k or 128k) requires a larger KV Cache in VRAM. If you plan to analyze huge PDF documents (RAG), allocate an extra 2-4GB of VRAM just for the context.
Frequently Asked Questions (FAQ)
What happens if I exceed my VRAM?
Most modern backends (like Ollama, LM Studio, or llama.cpp) support GPU Offloading. If a model needs 12GB but you only have 8GB VRAM, the software will put 8GB on the GPU and the remaining 4GB on your System RAM (CPU).
Result: The model will run, but it will be significantly slower.
Mac vs. Nvidia PC?
Nvidia (CUDA): The king of speed. RTX 3090/4090 cards are prized for their 24GB VRAM.
Mac (Apple Silicon): The king of capacity. Macs use “Unified Memory,” meaning if you buy a Mac Studio with 192GB RAM, the AI can use nearly all of it. Macs are slower than Nvidia GPUs but can run massive models (like Llama-3-405B) that no consumer PC can handle.
Which Quantization format should I choose?
If you are using Ollama or LM Studio, you are likely using GGUF format. We recommend starting with Q4_K_M (4-bit medium). If you have VRAM to spare, try Q6 or Q8.
(Params × Bits / 8) + (Context_Overhead) + (CUDA_Buffer). Results act as a safe estimate for GGUF/EXL2 formats.