AWQ 4-bit Model Size Calculator
Activation-aware 4-bit (AWQ) checkpoint sizing for vLLM/TensorRT-LLM deployments.
AWQ protects the ~1% of weight channels with the largest activation magnitudes by scaling them before 4-bit rounding — no backprop, no per-column solving. Same ~4.15 bpw as GPTQ-g128, often slightly better accuracy on instruction-tuned models.
Formula
Disclaimer: This tool is for general informational and estimation purposes only and is not professional financial, tax, accounting or legal advice. All figures are estimates — verify with a qualified professional before making decisions. Read the full disclaimer.
About AWQ 4-bit Model Size Calculator
AWQ protects the ~1% of weight channels with the largest activation magnitudes by scaling them before 4-bit rounding — no backprop, no per-column solving. Same ~4.15 bpw as GPTQ-g128, often slightly better accuracy on instruction-tuned models. 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 AWQ 4-bit Model Size Calculator
- 1Enter your values into AWQ 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 AWQ 4-bit Model Size Calculator?
- ✓Computes AWQ 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
AWQ vs GPTQ — practical difference?+
Both land at ~4.15 bpw. AWQ tends to preserve instruction-following slightly better (it never reconstructs against a dataset, reducing calibration overfit) and quantizes faster; GPTQ has broader kernel coverage on older stacks. On vLLM both are first-class.
Why 'activation-aware'?+
AWQ observes which input channels carry large activations at calibration time and rescales those channels so 4-bit rounding loses less of what the network actually uses. The insight: protecting 1% of salient channels recovers most of the quality gap.
Can I fine-tune an AWQ model?+
Not directly — AWQ checkpoints are inference artifacts. Fine-tune the FP16/BF16 base (or use QLoRA on NF4), then re-quantize with AWQ afterward. Re-run calibration on data resembling your fine-tune domain for best results.
What VRAM does a 70B AWQ need?+
Weights ≈ 70.6e9 × 4.15/8 ≈ 36.6 GB — runnable on a 48 GB A6000/L40S with healthy cache room, or on 2× 24 GB consumer cards with tensor parallelism. This calculator lets you check any parameter count instantly.
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