How to Use the GPU VRAM & Model Memory Estimator
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1
Select or input your model parameter count (1.5B, 8B, 14B, 32B, 70B, or 405B).
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Choose model quantization (FP16, Q8_0, Q5_K_M, Q4_K_M, or EXL2 3-bit).
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Adjust your target context window slider from 2,048 up to 131,072 tokens.
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Inspect total VRAM requirements, KV cache consumption, and matched GPU hardware recommendations.
Practical Example & Use Case
Determining if a Llama 3.3 70B model quantized at Q4_K_M with 8k context will fit onto a dual RTX 3090 (48GB total) workstation setup.
Technical Notes & Limitations
Calculations model standard Transformer attention architectures with GQA (Grouped Query Attention) and PyTorch/CUDA runtime buffers. Actual VRAM may vary by ±5% depending on driver versions.
Frequently Asked Questions
Can an 8B model run on an 8GB GPU like an RTX 3060/4060?
Yes. At Q4_K_M quantization with 8k context, an 8B parameter model consumes approximately 5.8 GB to 6.8 GB of VRAM, running smoothly on 8GB consumer GPUs.
What is the formula for calculating model weight VRAM?
Weight Memory (GB) ≈ Parameters (Billions) × Bytes per parameter × 1.05 overhead. At 4-bit quantization, each parameter requires roughly 0.55 bytes.
How does context window size impact VRAM?
As the context window expands, the Key-Value (KV) cache grows linearly. At 64k or 128k context, KV cache alone can exceed 10 GB of VRAM unless FP8 or Q4 cache quantization is enabled.