Model-Native Computing Architecture: Envisioning Future System Architecture Through the Lens of Computer Architecture
arXiv:2606.00288v1 Announce Type: new Abstract: Large language models are undergoing a transition from model technology to system technology. As developers use Codex, Claude Code, AutoGPT, and related agents to write code, manage projects, and execute multi-step tasks, recurring engineering problems such as cache reuse, context management, agent scheduling, and permission control increasingly resemble classical computer systems problems. This paper develops that analogy as a visionary survey. We map concepts from computer architecture to the emerging model-native stack and review work on LLM-a
The rapid advancement and adoption of large language models (LLMs) are pushing them beyond mere technological tools into foundational system components, necessitating a re-evaluation of their architectural underpinnings.
This paper highlights the growing complexity and systemic nature of LLM development, revealing that common challenges in AI agent development mirror classic computer architecture problems, demanding a more integrated systems-level approach.
The analogy to computer architecture re-frames the engineering problems facing LLMs and AI agents, suggesting a shift from model-centric to system-centric development that will influence future research and product design.
- · Computer architects
- · AI system designers
- · Hardware manufacturers
- · Cloud infrastructure providers
- · Purely model-centric AI developers
- · Legacy software architectures unable to integrate LLMs efficiently
Increased research and development into LLM-specific hardware and system optimization.
Emergence of new specialized processors and system architectures designed for model-native computing.
Potential for a new era of 'model-native' operating systems and application frameworks that fundamentally change how software is built.
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Read at arXiv cs.AI