Local Hardware

LLM VRAM Requirements: GPU Memory Calculations

VRAM is the primary bottleneck when hosting models locally. If your GPU runs out of VRAM, the system falls back to system RAM, degrading generation speeds. This guide details formulas to calculate VRAM requirements.

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1. The VRAM Formula

Total VRAM requirement is calculated as: `VRAM = Model Weights + KV Cache Memory + System Overhead`. Model weights represent the parameter size. The KV cache represents the conversation history tokens. System overhead represents VRAM consumed by your OS and display.

2. The Quantization Impact

Quantization compresses model weights: - **16-bit (FP16)**: 2GB VRAM per billion parameters. - **8-bit (INT8)**: 1GB VRAM per billion parameters. - **4-bit (INT4)**: 0.5GB VRAM per billion parameters.

3. KV Cache VRAM Math

At long context lengths, the KV cache consumes substantial memory. For a 70B parameter model with a batch size of 1, a 128k context consumes roughly 20GB of VRAM just to store the cache, illustrating the high memory requirements of long context tasks.

Frequently Asked Questions

How much VRAM does Llama 3 8B Q4 require?

Llama 3 8B at Q4 quantization requires roughly 4.8GB of VRAM to load, leaving space for system overhead on a standard 8GB graphics card.

What happens if I exceed my GPU's VRAM?

The model execution engine will crash or fallback to system RAM, reducing generation speeds significantly (from 50 tok/sec to 2 tok/sec).