Enter parameter count, source and target precision, KV-cache reserve, and batch size; Model Ruler estimates weight memory plus KV cache, a rough speedup, and an approximate quality delta. Quantization economics are hardware- and kernel-dependent — run evals on the exact stack before committing.
Estimates how precision changes affect model weight memory, KV-cache pressure, speedup, and approximate quality tradeoff.
Formula: weights_gb = params_billions * bytes_per_parameter; total_vram = weights_gb + kv_cache_estimate(batch_size, kv_cache_tokens)
Audit coverage: Self-host break-even · Eval-suite budget
What does this calculator compute?
It estimates the VRAM a model needs after a precision change, including KV cache, plus a rough speedup and an approximate quality delta.
What should I check before committing to a workload shape?
Lower precision reduces VRAM footprint and can raise throughput, at some quality risk. Check the VRAM target against your hardware and whether the quality drop at the chosen precision is acceptable for your task — the right quantization level is a memory-versus-quality decision, not a price decision.
Is this a quote or a benchmark?
Neither. It is a computed estimate. Real-world cost varies by rate-limit shape, caching, token-count precision, and provider-specific discounts not reflected in the public pricing table.
This calculator is driven by quantization constants, VRAM footprint, and quality-risk assumptions, not provider API prices. The numbers reflect memory and precision trade-offs; validate against your own hardware and target model before acting. View methodology.