SiMM is an open-source distributed KV cache engine that addresses GPU memory constraints in LLM inference by storing KV cache in RDMA-backed memory pools, achieving 3.1× speedup over no cache and up to 9× lower KV I/O latency on long-context multi-turn workloads.
The article argues that distributed systems theory (Amdahl's Law, CAP theorem, FLP impossibility) directly applies to AI agent coordination, proving that simply adding more agents cannot overcome fundamental mathematical limits on scalability. The solution is better system decomposition and reduced coupling, not raw agent count.
This article contrasts imperative (puppet master) versus declarative (octopus) orchestration models, arguing that top-down orchestration hits fundamental scaling limits due to combinatorial coordination overhead, while distributed declarative convergence scales linearly without centralized bottlenecks.