GGUF Q5_K_M Model Size Calculator
Size a Q5_K_M GGUF — the 'quality first' k-quant — for any parameter count, with RAM headroom.
Q5_K_M spends ~0.8 more bits per weight than Q4_K_M for measurably lower perplexity — the choice when you have RAM to spare but not enough for Q8_0. Effective rate ≈ 5.67 bpw including k-quant scales.
Formula
About GGUF Q5_K_M Model Size Calculator
Q5_K_M spends ~0.8 more bits per weight than Q4_K_M for measurably lower perplexity — the choice when you have RAM to spare but not enough for Q8_0. Effective rate ≈ 5.67 bpw including k-quant scales. This calculator turns any parameter count into a concrete file size and a realistic total-memory figure, so you can check whether a given checkpoint fits your GPU VRAM or system RAM before downloading tens of gigabytes. It uses the measured effective bits-per-weight of the format — including block scales and mixed-precision tensor exceptions — rather than the marketing bit-width.
How to use GGUF Q5_K_M Model Size Calculator
- 1Enter your values into GGUF Q5_K_M Model Size Calculator — sensible, domain-typical defaults are pre-filled so you see a real result immediately.
- 2The result recomputes live using the formula shown on the page; there is no button to press.
- 3Adjust any input to compare scenarios, then read the worked example to see the substituted numbers.
Why use GGUF Q5_K_M Model Size Calculator?
- ✓Computes GGUF Q5_K_M Model Size instantly in your browser — no sign-up, no upload, no server round-trip.
- ✓100% free and unlimited, with the exact formula shown: size(GB) = params × 5.67 bits ÷ 8 ÷ 10⁹ (5.67 = measured effective bits/weight for this format, incl. scales).
- ✓Runs entirely client-side, so every value you enter stays private on your device.
- ✓Live recompute as you type, with a worked example and authoritative references for trust.
Frequently asked questions
Q5_K_M vs Q4_K_M — is the upgrade worth it?+
Q5_K_M roughly halves the perplexity gap to FP16 versus Q4_K_M at ~17% more memory. For a 7B that is ~4.8 GB vs ~4.1 GB. If both fit your RAM, Q5_K_M is the safer pick for reasoning-heavy or code tasks.
Why do k-quants use fractional bits per weight?+
Weights are packed in super-blocks of 256 with shared 6-bit scales and mins; some tensor classes get a higher-precision format. Amortized over the whole file, that yields non-integer effective rates like 5.67 bits.
What hardware fits a 70B Q5_K_M?+
About 47 GB of weights — beyond any single consumer GPU, but fine on 64 GB system RAM (CPU inference), an A6000 48 GB with a thin margin and short context, or split across two 24 GB cards with GPU offload.
Is there a Q5_K_S too?+
Yes — the S (small) variant skips the higher-precision tensor exceptions for ~3% size saving and slightly worse quality. The M (medium) variants shown here are the ones the community generally distributes and benchmarks.
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