arXiv:2603.09555v2 Announce Type: replace 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: diagonal per-head dynamics, fixed-size chunking, einsum-dominated compute, and static control flow. Expressing this structure in standard JAX primitives gives a single-source inference path with no custom kernels, a registered JAX PyTree cache, and a compiled on-device autoregressive loop. On a single Google Cloud TPU v6e,
The rapid development of Mamba-2 and similar efficient AI architectures is driving research into more portable and accelerator-agnostic inference methods to broaden adoption.
This development improves the portability and efficiency of AI inference, reducing reliance on specific hardware vendors and custom kernels, which is crucial for democratizing access to high-performance AI.
AI models optimized for specific hardware can now be deployed more flexibly across different accelerator backends, potentially lowering operational costs and increasing accessibility.
- · AI developers
- · Cloud providers with diverse hardware
- · AI inference service providers
- · JAX framework developers
- · Companies reliant on proprietary custom kernels for AI acceleration
- · Hardware vendors with tightly coupled software stacks
Mamba-2 and similar models will see wider adoption due to easier deployment across various hardware platforms.
Increased portability reduces vendor lock-in for AI compute, fostering greater competition among hardware providers.
This could accelerate the development and deployment of sovereign AI initiatives by making high-performance inference less dependent on specific, hard-to-access hardware stacks.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG