Multi-agent LLM pipelines waste significant compute when agents re-prefill identical documents. Developer Anubhab Banerjee tackled this redundancy with SwarmKV, a C++ orchestration layer that runs prefill once and shares the KV cache across branches.

The approach serializes KV state via llama_state_get_data into a host buffer, then memcpy's it to per-branch allocations before decoding. This eliminates the quadratic cost of redundant dense attention passes that scale with prompt length.

Testing on a seven-year-old GTX 1080 showed dramatic results: the two-agent pipeline ran 48.69% faster end-to-end while the second agent's activation latency dropped 98.09% (roughly 52× improvement). The system eliminated 8,685 ms of duplicate computation without new transformer algorithms.

This represents classic systems engineering rather than novel ML research. The technique mirrors how 5G cell towers broadcast shared state every 80ms. For workloads like patent analysis with fifty evaluators over one 50,000-token specification, the savings multiply substantially.