GGUF Q8_0 Model Size Calculator
Near-lossless 8-bit GGUF sizing — when you want FP16 quality at half the memory.
Q8_0 stores straight 8-bit weights with one FP16 scale per 32-weight block (≈8.5 effective bpw). Perplexity is statistically indistinguishable from FP16 on most benchmarks — it is the reference quant people use to sanity-check smaller ones.
Formula
About GGUF Q8_0 Model Size Calculator
Q8_0 stores straight 8-bit weights with one FP16 scale per 32-weight block (≈8.5 effective bpw). Perplexity is statistically indistinguishable from FP16 on most benchmarks — it is the reference quant people use to sanity-check smaller ones. 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 Q8_0 Model Size Calculator
- 1Enter your values into GGUF Q8_0 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 Q8_0 Model Size Calculator?
- ✓Computes GGUF Q8_0 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 × 8.5 bits ÷ 8 ÷ 10⁹ (8.5 = 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
Is Q8_0 really lossless?+
Not mathematically, but in practice the perplexity delta versus FP16 is within measurement noise (≲0.01 on 7B models). If a behavior differs between your FP16 and Q8_0 runs, the cause is almost always sampling settings, not the quant.
When should I pick Q8_0 over Q4/Q5?+
When RAM is plentiful and you want to eliminate quantization as a variable: evaluation harnesses, regression-testing fine-tunes, or quality-critical production on CPU servers with abundant memory. Otherwise Q4_K_M/Q5_K_M serve better per GB.
Why 8.5 bits and not 8?+
Each 32-weight block carries a 16-bit FP scale: 32×8 + 16 = 272 bits per 32 weights = 8.5 bits per weight exactly. Q8_0 is the simplest GGUF format — no mins, no super-blocks, no mixed tensors.
Q8_0 GGUF vs INT8 GPTQ — same thing?+
Both are 8-bit, but GPTQ optimizes weights against calibration data and targets GPU kernels, while Q8_0 is a calibration-free round-to-nearest format for llama.cpp. Sizes are similar; ecosystems differ — pick by your inference stack.
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