Beyond the KV cache: Training and serving i14’s Laplace 3B linear attention model on AWS Trainium2 with native PyTorch

Managing runaway memory costs when serving long-context language models remains one of the steepest hurdles in production machine learning. In a new technical walkthrough, the engineering team at i14 demonstrates how they trained and deployed Laplace 3B—a linear attention architecture—on AWS Trainium2 using native PyTorch. The operational breakthrough lies in its memory footprint: while standard generative architectures require an ever-expanding key-value cache that scales with context length, Laplace maintains a constant recurrent state of just 29.5 megabytes per sequence. This eliminates out-of-memory spikes during generation and restores predictable, deterministic cost modeling to inference infrastructure. For engineering teams scaling data-intensive applications across the United States, constant-memory attention fundamentally shifts unit economics. Consider an enterprise health records platform in Cleveland processing continuous patient charts and real-time lab transcripts; shifting away from a dynamic cache allows engineers to pack dozens of concurrent long-context streams onto single accelerator instances without over-provisioning hardware to protect against memory thrashing. Similarly, a legal-tech startup in Chicago indexing multi-hundred-page discovery archives can retire brittle memory-swapping strategies, significantly simplifying their serving pipeline and cutting compute overhead. Even an internal IT analytics team in Austin monitoring high-volume industrial logs can unlock high-concurrency stream processing on hardware footprints that would otherwise choke on traditional transformer models. To evaluate whether this architecture fits your production roadmap, take one of your long-context