Interactive calculator predicting LLM decode speed and memory fit from the tiered decode law, with charts placing your machine among the validated measurements and showing the speed-memory trade-off.

Will it run — and how fast?

Pick a model, a bit-width, and a machine. This calculator applies the tiered decode law — fitted and validated on measurements from 7B to 744B, including two pre-registered hardware tests — to predict decode speed, memory fit, and quality cost before you download a single weight.

tok/s = η(tier) × bandwidth ÷ active-bytes-per-token

Configuration

Prediction

—tok/s (decode)
model size
—
active bytes/token
—
est. quality cost
—
All placements evaluated
The ladder — smartest model per speed band on THIS machine (bar length = model size in params; your target highlighted)
Your machine on the validated law
Validated points lie on the diagonal; your config is marked on it.
Speed ⇄ memory trade-off (bit-width sweep, capacity cliffs marked)
Lower bits: less memory and more speed, until quality cost; past RAM capacity, speed falls off a cliff.
Quality cost = perplexity gap-ratio vs fp16 with the depth-aware recipe (probe the fragile band, protect it). η bands are measured: VRAM/GPU per class · RAM ≈0.62 dense / ≈0.38 MoE · disk ≈0.95. Validation: 16B MoE 15.6 tok/s, 30B hybrid predicted 19 → measured 19.30 ± 0.88, 110B-from-SATA 0.19 (pre-registered 0.2–0.3), RAM overclock ×1.52 (pre-registered ×1.41+), colibri's published 744B tiers inside the same bands. Hybrid figures assume partitioned residency (--no-mmap + -ot exps=CPU). Estimates ±25%.
By @federico_sciuca — laws, probes, recipes and every raw log in the repo.