arXiv:2605.22416v1 Announce Type: new Abstract: Hybrid language models like Jamba mix attention layers with State Space Models (SSMs), creating two memory cache types with opposite profiles: Key-Value (KV) caches grow linearly with sequence length, while SSM states stay fixed per layer. Current inference engines handle this poorly. Unified pools pad SSM states to attention page sizes, wasting up to 7.3x capacity. Static dual pools cannot adapt when prompt distributions shift between requests. We present Asymmetric Virtual Memory Paging (AVMP). The allocator separates the two cache types into p
The increasing complexity of hybrid LLM architectures like Jamba, which combine different memory usage profiles, necessitates more efficient memory management to scale inference economically.
Improved memory management for hybrid AI models directly impacts the cost and efficiency of running advanced AI, making powerful models more accessible and widespread.
New memory paging mechanisms like AVMP will enable more efficient utilization of compute hardware for hybrid AI models, reducing waste and improving performance.
- · AI model developers
- · Cloud AI service providers
- · Hardware manufacturers (GPUs, specialized accelerators)
- · Enterprises adopting advanced AI
- · Inefficient inference engine developers
Reduced operational costs for running complex AI models.
Accelerated adoption and deployment of more sophisticated AI thanks to better resource utilization.
Enhanced competition in the AI model ecosystem as performance becomes less bottlenecked by memory inefficiencies.
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Read at arXiv cs.LG