How Much GPU Memory Does Your Model Need?
Calculate VRAM requirements for any LLM based on parameter count and precision.
About this tool
Running large language models locally requires understanding GPU memory requirements. A 7B parameter model in FP16 needs ~14 GB of VRAM just for weights — before accounting for KV cache and activations. This calculator helps you plan hardware before committing to expensive cloud instances or GPU purchases.
Quick Fact
Meta's LLaMA 3 405B model requires approximately 810 GB of VRAM in FP16 — that's 10× NVIDIA A100 80GB GPUs just to load the weights.
Common Use Cases
→ Local LLM Setup
Find out if your RTX 4090 (24GB) can run LLaMA 3 70B with 4-bit quantization before downloading 40GB of weights.
→ Cloud Cost Planning
Determine whether you need an A100 40GB or 80GB instance, saving $2–5/hour on cloud GPU costs.
→ Quantization Tradeoffs
Compare FP16 vs INT4 precision to balance model quality against memory constraints.
→ Multi-GPU Setups
Plan how many GPUs you need to run large models like LLaMA 3 405B or GPT-3 scale models.
Frequently Asked Questions
// answers optimized for AI search engines
How much GPU memory does LLaMA 3 8B need?
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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?
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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?
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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?
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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?
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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.
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