Laplace 3B

Change history

21 Oct 2026

AWS and i14 ditch the KV cache with a 3B linear-attention model on Trainium2

Parameter	Before	Now
Architecture	Transformer softmax attention with growing KV cache	Gated Linear Attention (GLA) with fixed-size recurrent state
Parameters	Not specified	3.028B (2.718B Muon + 309.6M AdamW)
Per-sequence memory state	KV cache grows with context length	Fixed 29.5 MB recurrent state regardless of context length
Training throughput	73,378 tokens/sec (gather-broadcast Muon)	119,934 tokens/sec (all_to_all Muon), ~120,000 tokens/sec overall on trn2.48xlarge
Inference throughput	Not specified	11,544 output tokens/sec across 1,280 concurrent sequences
Context window	2,048 training sequence length	Tested up to 8,192 tokens with only 0.5% extra cost per output token

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AWS and i14 ditch the KV cache with a 3B linear-attention model on Trainium2

AI research company i14 partnered with AWS to port its Laplace 3B model, a 24-layer architecture built on Gated Linear Attention (GLA) instead of standard transformer attention, onto AWS Trainium2 using native PyTorch (TorchNeuron Native). Rather than a KV cache that expands with every token, the model keeps a constant 29.5 MB recurrent state per sequence. On a single trn2.48xlarge instance, the team reached about 120,000 training tokens per second and served 11,544 output tokens per second across 1,280 simultaneous sequences through an unmodified vLLM engine.

2 hours agoAWS Machine Learning