Compute Lens

GPU cost per accepted output

Pay attention to the output, not the hourly rate.

Bring measured workloads and account for setup, egress, storage and failed outputs before choosing compute.

Use my own records

Local processing · No account · No wallet connection

What would you like to check?

Choose a fictional scenario to run it immediately, then change the assumptions.

Explore the calculations and common mistakes

Make it your own

Edit the form or import the example-shaped JSON. Input reference

Edit the inputs

Inputs stay in this tab and clear on reload. No automatic upload or wallet access. Maximum 128 KiB.

Report text for manual copy

From your records to a reviewable result

  1. Run identical fixtures with the same weights, precision and acceptance rule.
  2. Record all billed setup/run time and ancillary costs, including failed attempts.
  3. Compare eligible runs and investigate any changed workload assumptions.
Read the method and example

Where this tool fits

A workload-level bill review that includes preparation and rejected results. It can compare your own cloud and decentralized measurements in one format.

Scope: No live GPU inventory, automatic benchmarking, reservation, infrastructure management or verified savings.

去中心化算力采购：可直接使用表单，也可导入 JSON。先运行虚构示例理解结果，再切换到自己的材料。输入仅在当前页面处理。查看中文说明。

Compare with established tools

Buy from an infrastructure provider when you need compute. This tool helps review the economics of measurements; it cannot supply hardware or prove a workload is representative.

Official product descriptions reviewed 19 September 2026. These are alternatives, not partners or endorsements.

  • Akash

    Decentralized compute marketplace with resource configuration, provider bidding and deployment.

  • Runpod

    GPU cloud offerings with separate compute and storage pricing.

What does Compute Lens do?

Compute Lens compares measured workload runs using total entered compute, setup, storage and egress costs per accepted output. It filters mismatched workload labels, currencies, quality and memory limits. All examples are fictional; no live GPU quotes are supplied.

By the AGI Scorecard team. Method and sources reviewed . Read the method.

Questions before you start

Why can a cheaper GPU have a higher cost per useful result?

Longer setup, slower execution, rejected outputs or data-transfer costs can outweigh the hourly discount. Compare the same workload and acceptance rule, and include all billed time before dividing by accepted outputs.

Is Compute Lens free, and do I need a wallet?

All current functions are free beta. No account, wallet connection, subscription or model API key is needed. No live GPU inventory, automatic benchmarking, reservation, infrastructure management or verified savings.

Can I use my own records and keep them private?

Yes. Enter records, import JSON or paste CSV into record groups. Inputs and comparison snapshots stay in this browser tab. Share-example links contain only a public scenario name. Review downloaded reports before sharing your records.

Continue your review

Route LabCompare bounded supplier sequences using quality assumptions, worst-case cost and latency constraints.Proof PlanPlan a zkML handoff with measured proving time, verification cost and a pinned model/input/key manifest.

Use this workflow in your AI assistant

Connect the MCP server to read sources and run these calculations from a supported client. Remote calls send parameters to the server; the browser worksheet remains local.

Get the MCP connection and citation guide