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Compiler-first duality enables portable O(1) Mamba-2 inference

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new method for optimizing Mamba-2 inference, focusing on compiler-first state space duality. This approach enables portable autoregressive caching with $O(1)$ complexity, eliminating the need for custom CUDA or Triton kernels. The resulting single-source inference path, implemented in JAX, demonstrates significant speedups on Google Cloud TPUs and NVIDIA GPUs, achieving high hardware utilization and matching reference perplexity scores. AI

IMPACT Enables faster and more portable inference for large state space models, potentially reducing deployment costs and complexity.

RANK_REASON Academic paper detailing a novel inference optimization technique for state space models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Compiler-first duality enables portable O(1) Mamba-2 inference

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Academic paper detailing a novel inference optimization technique for state space models. [lever_c_demoted from research: ic=1 ai=1.0]
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107 days old
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Cosmo Santoni, Anmol Thapar ·

    Compiler-First State Space Duality and Portable $O(1)$ Autoregressive Caching for Inference

    arXiv:2603.09555v2 Announce Type: replace-cross Abstract: High-throughput Mamba-2 inference is usually tied to fused CUDA and Triton kernels, limiting portability across accelerator backends. We show that the state space duality (SSD) recurrence has a compiler-friendly structure:…

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