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This competition directly addresses the critical intersection of hardware constraints and model architecture by challenging researchers to co-design LLMs specifically for AWS Trainium's unique systolic array and memory hierarchy. By shifting bottlenecks from memory-bound to compute-bound operations, it explores how fundamental changes in silicon geometry can redefine optimal attention mechanisms and parallelism strategies beyond current GPU paradigms.
AWS Trainium Frontier competition: Co-design models and kernels on purpose-built AI chips
A competition with a finalist ceremony during NeurIPS 2026, challenging researchers to train language models from scratch on Trainium, exploring what optimal architectures look like when the hardware changes.
Modern LLM architectures have co-evolved within a single hardware family. The shapes of our attention mechanisms, the structure of our multilayer perceptrons (MLPs), the choice of numerical formats, and even the granularity of parallelism strategies have all been shaped by hardware constraints: warp sizes, tensor core geometries, memory hierarchies, and the kernel abstractions those chips expose. When the hardware changes, the efficient frontier of model architectures changes with it. Here we present an opportunity for academic and industry labs to explore this frontier in detail on AWS Trainium. Purpose-built accelerators like AWS Trainium present a genuinely different design surface. More on-chip SRAM (SBUF), explicit software control over data movement and acceleration at the…
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