v1.0.0

First tagged release of the LLM Inference Estimator: a single-page tool that sizes one server for an open LLM, your user count, workload and speed goal.

Use it

• Hosted: https://nvme101010.github.io/LLM-Inference-Estimator/
• Container: docker run --rm -p 8080:8080 ghcr.io/nvme101010/llm-inference-estimator:1.0.0 then open http://localhost:8080. Images for linux/amd64 and linux/arm64; tags 1.0.0, 1.0 and latest.
• Single file: download index.html from the source below and open it in a browser.

What's in 1.0.0

• Layout. Fewer tooltip icons, numbers in tabular figures, one-line input hints. The suggestion panel shows three figures and explains capacity in a sentence. "Every board" is a table with the reasoning behind each row's GPU count behind a chevron. "Speed on this setup" lives under "Show the math".
• Boards. H200 NVL (1, 2, 4 or 8 cards per server; an NVLink bridge joins at most 4, so one copy of a model spans at most 4 and 8 cards run two copies), HGX H200, HGX B200 and HGX B300 (8-GPU systems joined through NVSwitch), L4, L40S, RTX PRO 4500 and 6000 Blackwell, AMD MI350P. The H100 is dropped as end of life.
• Not recommended layouts, where one copy would be split across PCIe-only cards, are marked in red and explained, but stay pickable.
• Explanations. Rows say what a split means ("both serve every user together") versus copies ("each serving ~300 of them"). The math section states that speed per user is 1 ÷ one decode step, with every active request getting one token per step.
• Container. nginxinc/nginx-unprivileged image, ~21 MB, non-root, read-only root filesystem, health check, gzip, security headers. A GitHub Actions workflow builds and smoke-tests every pull request and publishes to GHCR on merges and tags.

Model and board data as of October 2026. Estimates for planning, not a guarantee.