i14 Trains and Serves Laplace 3B Linear‑Attention Model on AWS Trainium2

i14’s Laplace 3B, a 3‑billion‑parameter linear‑attention language model, was trained on a single trn2.48xlarge AWS Trainium2 instance at roughly 120,000 tokens per second. During inference, the model delivered 11,544 output tokens per second across 1,280 concurrent sequences while keeping a fixed 29.5 MB recurrent state per sequence, independent of context length. The work leveraged TorchNeuron Native to port i14’s Gated Linear Attention (GLA) kernels to Trainium2, preserving the model’s Muon optimizer and custom architecture.