Training and Serving Laplace 3B Linear Attention Model on AWS Trainium2
WebCrimp analysis and practical explanation. AI-assisted; reviewed before publication.
Quick Summary
The article explains how i14 adapted its Laplace 3B model, which uses Gated Linear Attention instead of softmax, to run on AWS Trainium2. By rewriting the attention kernel in native PyTorch, optimizing Muon communication with all_to_all, and unrolling the backward pass, they achieved ~120k tokens/s training and 11.5k tokens/s inference with a fixed 29.5 MB state per sequence.
What Happened?
i14 and the AWS Generative AI Innovation Center replaced the GPU‑optimized Triton GLA kernel with standard PyTorch operators, redesigned Muon's distributed orthogonalization to use all_to_all collectives, and statically unrolled the 16‑chunk recurrence to fit Trainium2's compiler constraints. Training on a single trn2.48xlarge instance reached about 120,000 tokens per second, and inference via an unmodified vLLM engine delivered 11,544 output tokens per second across 1,280 concurrent sequences.
Why It Matters
Linear attention eliminates the quadratic growth of the KV cache, providing predictable memory usage and lower serving costs for long‑context workloads. This case study shows that custom architectures can be ported to specialized accelerators like Trainium2 while preserving high throughput and model fidelity.
Who Can Use It?
AI/ML · Cloud · DevOps · Software Development · Data Analytics
How Can I Use This?
If you work in AI/ML, Cloud, DevOps, Software Development, Data Analytics
- AI/ML engineers can evaluate linear attention for tasks requiring long context, such as document analysis or agent memory. Cloud architects can design serving infrastructure that exploits fixed‑size state for cost savings. DevOps teams can adopt the chunk‑size alignment technique to optimize on‑chip memory usage. Software developers can integrate similar all_to_all patterns to reduce communication overhead in distributed training.
AI Tip
When implementing GLA on Trainium2, set the chunk size to 128 tokens so each chunk maps one‑to‑one onto the 128 SBUF partitions, improving throughput by about 14%.
AI Trick
Replace the fused Triton kernel with explicit PyTorch loops over 16 chunks; this enables the static compiler to unroll the recurrence and avoid runtime graph construction.
Example Workflow
- 1. Define the GLA recurrence with fixed chunk count (16) and chunk size (128). 2. Implement the intra‑chunk masked linear attention and sequential state update using standard PyTorch ops. 3. Compile with TorchNeuron Native, ensuring the graph fits the scheduler and on‑chip memory. 4. For distributed training, replace all_gather/broadcast in Muon with all_to_all transposition. 5. Verify training stability and measure throughput; then serve with vLLM using a custom wrapper to manage the recurrent state.
Copy This Prompt
I am building a linear attention model for [task] with a fixed context length of [N] tokens. I want to train it on AWS Trainium2 using native PyTorch. Please provide a step‑by‑step guide that includes: (1) setting chunk size and count, (2) rewriting the attention kernel without Triton, (3) configuring Muon optimizer communication, and (4) unrolling the backward pass. Include code snippets for each part.
Recommended Tools
- AWS Trainium2
- PyTorch
- TorchNeuron Native
- vLLM
- NeuronCore
- MuonFSDP2
Check official documentation, availability and pricing before using these suggestions.
Difficulty
Advanced
Estimated Time Saved
Not measured; validate with your own workflow.
Source
AWS AI
Beyond the KV cache: Training and serving i14’s Laplace 3B linear attention model on AWS Trainium2 with native PyTorch
Original publication date: 2026-10-20T21:14:10+00:00