GPTQ 4-bit Model Size Calculator
VRAM footprint of GPTQ-quantized checkpoints (group size 128) for GPU serving.
GPTQ quantizes weights one column at a time against calibration data, minimizing layer-output error. With the standard group size of 128, FP16 scales add ~0.15 bits — ≈4.15 effective bpw, served by exllama/Marlin GPU kernels.
Formula
About GPTQ 4-bit Model Size Calculator
GPTQ quantizes weights one column at a time against calibration data, minimizing layer-output error. With the standard group size of 128, FP16 scales add ~0.15 bits — ≈4.15 effective bpw, served by exllama/Marlin GPU kernels. 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 GPTQ 4-bit Model Size Calculator
- 1Enter your values into GPTQ 4-bit 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 GPTQ 4-bit Model Size Calculator?
- ✓Computes GPTQ 4-bit 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 × 4.15 bits ÷ 8 ÷ 10⁹ (4.15 = 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
GPTQ vs GGUF Q4_K_M — which is smaller?+
GPTQ at group 128 (~4.15 bpw) is ~14% smaller than Q4_K_M (~4.84 bpw) because it keeps every tensor at 4 bits. Quality is comparable; the real decision is your runtime — GPTQ for GPU-only stacks (vLLM, ExLlama), GGUF for llama.cpp.
What does group size change?+
One FP16 scale is shared per group of weights per output channel. Group 128 is standard; group 32 improves accuracy (~4.6 bpw) and -1 (per-channel) shrinks further but hurts quality. This tool's 4.15 figure assumes group 128 with act-order.
Does GPTQ need calibration data?+
Yes — typically 128–512 short samples (often C4 or WikiText). The quantizer uses them to weight which directions of error matter. Bad or mismatched calibration (e.g. English-only for a code model) measurably hurts the quantized model.
Why is my GPTQ VRAM higher than the file size?+
Dequantization workspace, the KV cache and CUDA context add overhead. Budget file size + ~1.2 GB runtime + cache (use our per-model KV-cache calculators) — that is the number that decides whether a 24 GB card suffices.
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