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Models2026-08-28 · source-backed
A 1,134-upvote r/LocalLLaMA post pushes back on the claim that n-gram tables let you run 1T+ models with 980B parameters offloaded to SSD (r/LocalLLaMA). An engram is an embedding table keyed on the last two or three tokens rather than one token ID, so "New York" gets a memorized vector via an O(1) hash lookup with no FLOPs, freeing early transformer layers from re-deriving multi-token entities. Because lookups cost no compute, you can quantize weights to Q4_K_XL while keeping the engram table at native precision. Commenters report the practical tell is better letter counting with minimal reasoning and better negation handling.
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Shared entities / Same source domain / Shared topic / Earlier coverage
Both cover Commenters, LocalLLaMA; reported by the same outlet (reddit.com); overlapping topics (commenter, cost, token).
Both cover FLOPs, LocalLLaMA; reported by the same outlet (reddit.com); overlapping topics (cost, model, token).
Both cover LocalLLaMA, SSD; reported by the same outlet (reddit.com); overlapping topics (claim, early).
Shared entity: LocalLLaMA / Same source domain / Shared topic / Earlier coverage / Tension
Both cover LocalLLaMA; reported by the same outlet (reddit.com); overlapping topics (cost, model, token).
Shared entities / Earlier coverage
Both cover Commenters, LocalLLaMA, SSD; earlier Commenters coverage from 2026-08-26.
Shared entity: LocalLLaMA / Same source domain / Shared topic / Earlier coverage / Tension
Both cover LocalLLaMA; reported by the same outlet (reddit.com); overlapping topics (back, depth).
Both cover LocalLLaMA; reported by the same outlet (reddit.com); overlapping topics (better, model).
Shared entity: LocalLLaMA / Same source domain / Shared topic / Earlier coverage
Both cover LocalLLaMA; reported by the same outlet (reddit.com); overlapping topics (commenter, model, token).