VRAM Calculator 2026: 7B/13B/70B LLM GPU Requirements (Q4, Q5, Q8)

VRAM required (GB) equals model parameters in billions multiplied by quantization bits (16 for FP16, 8 for Q8, 4 for Q4, etc.), divided by 8.

Think of VRAM like bookshelf space. Bigger books (models with more parameters like 70B) take more shelf space. Smaller books (Q4 quantization) take less space than larger ones (FP16). The formula tells you exactly how many "shelves" (GB) you need. Always leave extra empty shelves for conversations, multiple requests at once, and system software.

The formula for VRAM requirement is deceptively simple:

💡 Pro Tip: This formula calculates model weights only. Real VRAM usage is 25–40% higher due to context, batching, and system overhead. Always add a safety margin.

Use this calculator to compute exact VRAM requirements for any combination of model, quantization, context, and batch size. Select your configuration and see which GPUs fit.

🔍 Key Insight: Quantization trades file size for quality. Q5 is the sweet spot (95% quality, 68% smaller). Q4 is acceptable for most users. Q3 and below are only for edge devices or when VRAM is critically constrained.

Practical VRAM calculations for common scenarios:

⚠️ Warning: These calculations are for model weights only. Add 25–40% for context, batch processing, and system overhead. Example: 13B Q5 = 8.1 GB model + 2–3 GB overhead = 10–11 GB actual.

The formula calculates model weights only. Your actual VRAM usage will be higher due to several factors. Budget an additional 25–40% beyond the calculated amount.

Context window (key-value cache) stores conversation history during inference. A 4k-token context uses approximately 2–3 GB for a 7B model.

📌 Key Point: Batch processing increases VRAM usage linearly. Each additional concurrent prompt (when processing multiple requests simultaneously) uses 500 MB–2 GB of extra memory. If you run batch=4, multiply the single-request VRAM by 4 and add overhead.

System overhead from the operating system and inference engine framework (Ollama, vLLM, llama.cpp) reserves 500 MB–1 GB. Always maintain a safety margin when choosing a GPU.

Use the interactive calculator above to find your exact fit. Below are common GPU scenarios and recommended models.

The formula is accurate within ±10% for most cases. Real-world VRAM usage varies based on implementation, model architecture, and inference engine optimizations.

Sources of variation include: different quantization formats (GGUF vs. safetensors vs. AWQ), model architecture (Transformer vs. non-Transformer), and inference engine-specific optimizations (vLLM, llama.cpp, Ollama).

Treat the formula as a conservative estimate. Always add a 25% safety margin when purchasing GPUs to account for context overhead, batching, and system processes.

European Union (GDPR): Local inference (on-premises) ensures data residency compliance under GDPR. Running models on your own GPU keeps user data in-country. This VRAM calculator helps you size hardware for privacy-first deployments.

Japan (APPI): The Act on Protection of Personal Information (APPI) requires careful data handling. On-device LLM inference reduces data transfer and processing outside Japan. Use this calculator to size systems for Japanese enterprise deployments.

China (Data Security Law): China's 2021 Data Security Law mandates data residency within Chinese borders. Local LLM inference on domestic servers (Alibaba Cloud, Tencent Cloud) is compliant. This formula applies to sizing those deployments with Chinese-optimized models like Qwen3.

In all regions, local inference provides stronger data privacy guarantees than cloud APIs. This VRAM calculator is essential for designing compliant, privacy-preserving AI systems.