i14's new AI keeps memory use flat however long the conversation runs
Why it matters
If you serve long-context or agent workloads, i14's design keeps memory per session flat at 29.5 MB, so serving cost stops growing with history.
Ask your team
Does memory use from long conversations drive our AI serving costs, and would a fixed-memory design change that?
What happened
i14 built Laplace 3B, a language model whose memory per conversation stays fixed at 29.5 MB however long the context gets. With AWS, it trained and served the model on Trainium2 chips using native PyTorch.
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