Tokenstead

Tokenstead is a web-based discovery interface that matches open-weight AI models to the hardware you already own. The core workflow is three steps: pick your rig from a pre-configured list or enter custom specs, review which models fit with honest speed estimates, then compare the cost of running locally against equivalent cloud API spend. GitHub sign-in is free and lets you save your rig configuration for return visits.

The differentiating feature is the adopter tracker — 22 real-world deployments logged with source citations, distinguishing between confirmed (official source), reported (credible third-party), and testing (not yet deployed) statuses. That distinction matters: most model directories list what’s theoretically possible; this one logs what organizations are actually running in production, with enough detail to evaluate whether the use case resembles yours.

Tokenstead fits cleanly into the research and scoping phase of a local AI deployment: you are trying to answer ‘can my hardware run this model, and does it make financial sense to run it myself?’ It does not answer ‘how do I run it’ beyond pointing you toward the model. There is no API, no hosted inference, no deployment tooling, and no automated benchmarking against your physical hardware — the speed estimates are the vendor’s projections, not measured throughput on your specific rig.

The hardware list, per the page, covers unified-memory devices including Apple Silicon Mac variants and Jetson-class hardware, discrete GPU setups, and system RAM configurations. The model list tracks 34 models with parameter counts, context lengths, and quality scores. Both lists are curated manually, not scraped, which keeps the data clean but means coverage lags new model releases by however long curation takes.