repoGitHubTrust 82 · PrimaryPublished yesterdayLive · 23h ago
mlhher/late-cli
Orchestrate an entire AI dev team on 5GB VRAM. Ephemeral subagents, exact-match diffs. Single static binary, any model. Zero config, zero context bloat.
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
Covers
newsI mapped which local LLMs actually fit each RAM tier, 8 to 128GB (open dataset)newsHypothetically speaking...newsLiquid AI's smallest model yet LFM2.5-230M beats models 4X its size at data extraction, can run 'anywhere'newsDevs - you have 64gb of VRAM - which model do you use for coding?newsBiggest, baddest model to fill 144GB VRAM + 120GB RAM to the brim, regardless of speed
Related across the graph
newsLiquid AI's smallest model yet LFM2.5-230M beats models 4X its size at data extraction, can run 'anywhere'newsI mapped which local LLMs actually fit each RAM tier, 8 to 128GB (open dataset)newsDevs - you have 64gb of VRAM - which model do you use for coding?newsHypothetically speaking...newsBiggest, baddest model to fill 144GB VRAM + 120GB RAM to the brim, regardless of speed