How accurate is this VRAM calculator?

Our calculator uses empirically-tested formulas derived from real-world measurements with llama.cpp. The accuracy is typically within 5% of actual VRAM usage, making it reliable for hardware planning.

Can I run larger models by using system RAM?

Yes, but performance will be severely impacted. GPU-to-RAM transfers are much slower than VRAM access. This approach is only viable for occasional use or when inference speed isn't critical.

Do MoE models like Mixtral require loading all parameters?

Yes, despite being 'mixture of experts,' current implementations load all expert parameters into VRAM. A 8x22B Mixtral model requires similar VRAM to a dense 141B model, not just the active expert subset.

What's the difference between this and online VRAM calculators?

Our calculator uses the latest empirical formula that accounts for GQA (Grouped Query Attention), specific model architectures, and real-world overhead measurements. Many online calculators use simplified estimates that can be significantly inaccurate.

Which quantization format should I choose?

Q4_K_M provides the best balance between model size and quality for most use cases. Use Q5_K_M or Q6_K if you have extra VRAM and want better quality. F16 is only recommended if you have abundant VRAM and need maximum quality.

How does context length affect VRAM usage?

Context length has a linear impact on VRAM through the KV cache. Each 1,000 tokens typically consume ~0.11GB additional VRAM for a 7B model. Long conversations can quickly exceed model weight memory usage.

VRAM Calculator for Local Open Source LLMs - Accurate Memory Requirements 2025 | LocalLLM.in

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Published on January 20, 2025

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