Open formula. Browser-based calculations. Source-dated rates. No API keys. No provider login. No tracing SDK. No account required. Input payloads are not stored.
Pricing source date: 2026-05-28 · Build verification: 2026-05-28 · Stale threshold: 30 days. 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.
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)
Primary source register
Quantization economics are hardware/kernel dependent. Use this as a planning estimate before running evals on the exact model and deployment stack.
- https://openai.com/pricing
- https://www.anthropic.com/pricing
- https://platform.claude.com/docs/en/about-claude/pricing
- https://platform.claude.com/docs/en/about-claude/models/overview
- https://cloud.google.com/vertex-ai/generative-ai/pricing
- https://www.together.ai/pricing
- https://replicate.com/pricing
- https://fireworks.ai/pricing
- https://groq.com/pricing
- https://deepinfra.com/pricing
- https://aws.amazon.com/bedrock/pricing/
- https://azure.microsoft.com/pricing/details/cognitive-services/openai-service/
Included assumptions
- parameter count
- source precision
- target precision
- KV-cache reserve
- batch size
Excluded assumptions
- kernel support differences
- activation memory
- serving framework overhead
- application-specific eval results
Architecture-cost audit
Self-host break-evenGPU hourly cost, utilization, throughput, operational overhead, and API comparison.
Eval-suite budgetSamples, candidate models, repeated trials, and optional LLM-as-judge pass costs.
Diagnostic output
This tool returns Cost classification, Dominant cost driver, Decision threshold, and Sensitivity. Diagnostic focus: VRAM-fit threshold and quality-risk sensitivity..
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