🧠 AI Tool
Model Size Estimator
See how much GPU memory you’ll need from parameter count (7B, 70B, …) and precision—before you download or rent hardware.
FAQ
Frequently asked questions
Detailed answers below are in English for technical accuracy.
How much GPU memory does LLaMA 3 8B need?▼
LLaMA 3 8B requires approximately 16 GB of GPU memory in FP16 precision (including overhead). With 4-bit quantization (INT4 or GGUF Q4), memory drops to around 5–6 GB, making it runnable on consumer GPUs like the RTX 3060.
How do I calculate GPU memory for an LLM?▼
Multiply the number of parameters by bytes per parameter: FP32 uses 4 bytes, FP16/BF16 uses 2 bytes, INT8 uses 1 byte, INT4 uses 0.5 bytes. Then add ~20% for KV cache and activations. Example: 7B parameters × 2 bytes (FP16) = 14 GB + 20% overhead = ~17 GB total.
Can I run a 70B model on a single GPU?▼
A 70B model in FP16 requires ~140 GB of VRAM — more than any single consumer GPU. However, with 4-bit quantization, memory drops to ~35–40 GB, which fits on a single NVIDIA A100 80GB or two RTX 4090s combined.
What is model quantization?▼
Quantization reduces the numerical precision of model weights, shrinking memory requirements at a small quality cost. Common formats include INT8 (2× smaller than FP16), INT4 (4× smaller), and GGUF Q4_K_M (a popular format for llama.cpp). Most 4-bit quantized models retain 95%+ of the original quality.
What GPU do I need to run LLMs locally?▼
For 7B models in 4-bit quantization, an 8–12 GB VRAM GPU like the RTX 3060 or 4060 is sufficient. For 13B models, 16–24 GB (RTX 3090, 4090). For 70B models, either multiple consumer GPUs or a professional A100/H100 is required.