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VRAMwise

How we calculate VRAM requirements

Every verdict on this site should be reproducible from plain text.

Weights

Prefer published GGUF sizes per quant from our model table. If missing, tools that need a size ask you to enter one rather than inventing silently.

KV cache

From architecture fields when present: bytes per token times context length.

Overhead

Default 10% on (weights+kv). Increase for vision stacks or concurrent batches.

What we do not claim

Exact tokens/sec, multi-GPU sharding, or vendor marketing “AI TOPS” conversions.

General information, not personalized advice.

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Catalog exits

Frequently asked questions

What are the three terms?

Weights (GGUF size), KV cache, and a runtime overhead percentage.

Why overhead?

Frameworks keep scratch buffers beyond pure weights and KV.

Why FITS vs TIGHT?

TIGHT means total ≤ VRAM but headroom < 10% of VRAM — easy to break with a longer context.

Do you support Apple Silicon fully?

Unified memory differs; discrete GPU VRAM is the first-class path in the fit checker.

Advice?

Methodology transparency, not a warranty.

How We Calculate VRAM Requirements — by VRAMwise ↗