The Local LLM Renaissance
Running LLMs locally is no longer just for researchers. With the explosion of powerful 8B-70B open-weight models, developers are running inference on consumer GPUs. But the most common question remains: "Will this model fit in my VRAM?"
The Math Behind VRAM
Calculating VRAM isn't just about the model file size. You have to account for:
- Model Weights: (Parameters × Bytes per Parameter). A 7B model at FP16 takes ~14GB. At Q4_K_M (4-bit), it takes ~4.2GB.
- KV Cache: The memory required to store the context window. This scales linearly with context length and batch size.
The Free Calculator
Stop guessing and getting CUDA Out Of Memory (OOM) errors. We built a 100% client-side Local LLM VRAM Calculator on OmniTool Hub.
Just input the model parameters (e.g., 8B, 70B), select your quantization (FP16, Q8, Q4), and your target context length (e.g., 8k, 32k). It instantly calculates the exact VRAM you need.
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