inference — Running a trained model to get answers — the phase where AI is actually used, as opposed to trained.
open weights — A model whose trained parameters are published for anyone to download and run — unlike API-only models you can access but never possess.
benchmark — A standard public test set for comparing AI models — the shared scoreboards behind every "model X beats model Y" claim.
Why it matters
If you're choosing between open weightsA model whose trained parameters are published for anyone to download and run — unlike API-only models you can access but never possess.Full definition → models for a project, this framework helps you see past marketing and benchmarkA standard public test set for comparing AI models — the shared scoreboards behind every "model X beats model Y" claim.Full definition → rankings to what's actually disclosed — weights, training data, architecture code — so you can judge fitness for your specific need (auditability vs. efficiency vs. customization).
Key quotes
“We are no longer moving toward one universally "best" open model. Labs now optimize for four different outcomes: deep transparency, hardware efficiency, modular specialization, or raw frontier capability.”
Akruti Acharya
“Frontier-first releases like Kimi K3 (2.8T parameters) and DeepSeek V4 Pro (1.6T parameters) match closed-model capability but are practically undeployable on standard hardware despite being freely downloadable.”
Akruti Acharya
“The word "open" has quietly stopped describing a license and started describing a stack.”
Akruti Acharya
“active parameter count and memory footprint are separate properties that rarely move together, meaning low compute cost does not equal small hardware requirements”
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