Tokenstead
Summary
Picking a local model without knowing your hardware ceiling is how you end up downloading 70GB of weights that your GPU stalls on at inference — Tokenstead exists to close that gap before you waste the bandwidth.
Select your rig from a list of 56 tracked hardware configs — Mac unified-memory devices, multi-GPU setups up to 8x, or custom specs — and the site surfaces which of its 34 tracked open models fit, with speed estimates and a side-by-side comparison of running locally versus paying cloud API rates. The adopter tracker adds sourced, real-world deployment cases: confirmed self-hosted Llama, Codestral, and Mistral runs at named organizations, not anonymous forum posts. Where it stops: this is a discovery and planning interface, not a deployment tool. It tells you what fits; you still wire up the inference stack yourself. Teams who need automated model benchmarking on their actual hardware, or who want to pull model recommendations programmatically, hit a wall — there is no API.
Bottom line: The right tool for scoping a local inference setup before you commit hardware budget — but the moment your team needs programmatic access to the matching logic or wants to run automated benchmarks against your actual stack, you are back to building that layer yourself.
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Pros
Sign in to edit- Hardware-to-model matching across 56 tracked configs — including Apple Silicon unified-memory and multi-GPU setups — so you know before downloading whether a model fits your ceiling rather than finding out mid-load.
- Side-by-side own-vs-rent cost comparison built into the selection flow, so you can make the local-vs-cloud call with actual numbers rather than gut feel.
- Sourced adopter tracker with confirmed/reported/testing status distinctions, so you can vet whether a deployment case actually matches your use case instead of taking an anonymous claim at face value.
- Free rig-saving via GitHub sign-in with no paid tiers described, so you are not gated behind a paywall to use the core matching functionality.
- Context length and quality scores surfaced alongside parameter counts, so you can filter models by what your workload actually needs rather than defaulting to the largest model your hardware can technically load.
Cons
Sign in to edit- Speed estimates are projections, not measured throughput on your physical hardware — teams running latency-sensitive workloads cannot trust the numbers until they benchmark themselves, which means Tokenstead answers 'will it fit' but not 'will it be fast enough.'
- No API means the matching logic cannot be pulled into internal tooling, a model-selection pipeline, or a team dashboard — engineering teams who want to automate hardware-to-model decisions build their own lookup layer and stop returning to the site.
- 34 tracked models and 56 hardware configs is a curated, manually maintained list — when a significant new open-weight model ships, it is absent until the curation catches up, which means teams evaluating frontier releases at release time switch to Hugging Face or LM Studio's model browser where coverage is broader and faster.
- No self-hosted option and no local installation path — teams in air-gapped environments or with strict data-residency requirements on what tools their engineers access cannot use the site at all.
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About
- Platforms
- Web
- API Available
- No
- Self-Hosted
- No
- Last Updated
- 2026-07-11T04:29:30.692Z
Best For
Who it's for
- Users with specific GPU or unified-memory hardware
- Local AI deployment planning
- Comparing model quality scores and context lengths
What it does well
- Match open models to owned hardware specs
- Compare local run feasibility versus cloud rental costs
- Track real-world deployments of open models by organizations
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Frequently Asked Questions
- Is Tokenstead free?
- Yes — Tokenstead is fully free to use. There is no paid tier.
- Is Tokenstead open source?
- Yes. Tokenstead is open source.
- What platforms does Tokenstead support?
- Tokenstead is available on: Web.
Hours Saved & ROI Stories Community
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Curated lists that include this category
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.