TABLE OF CONTENTS [TAP TO EXPAND]
Co-Located Local Agent Clusters
Modern AI workflows increasingly deploy multi-model pipelines on local workstation nodes or dedicated edge servers. A supervisor model, code-generation agent, test validator, and vector search process all run concurrently on the same host machine.
The Serialization Tax
When co-located processes communicate over standard JSON HTTP REST endpoints or gRPC sockets, up to 40% of runtime CPU time is consumed encoding large tensor arrays into JSON strings and decoding them back into memory.
Limits of Standard Sockets
Unix domain sockets reduce kernel network stack overhead but still require memory buffer copy cycles between user space and kernel space.
Shared-Memory Ring Buffers
HIRAX investigated a zero-copy POSIX shared-memory (`shm_open`) ring buffer architecture allowing supervisor and worker processes to access identical tensor memory addresses concurrently.
Memory Pipeline Components
Direct SHM allocators allocate fixed-size pinned physical memory pages accessible across process boundaries with lock-free atomic futex rings coordinating read/write access.
Zero-Copy Data Transfer
When a supervisor agent outputs an activation tensor, it writes directly to the shared memory ring buffer. Worker agents read the exact memory pointer without a single byte copy or JSON serialization step.
Latency Benchmarks
Evaluated on multi-agent test rigs exchanging 100MB embedding batches across 8 co-located processes.
Demonstrated Results
Achieved near-zero transport latency (0.02ms) between co-located supervisor and agent processes, reducing CPU serialization overhead by over 90%.
Host Boundary Constraints
Shared memory IPC operates exclusively on co-located single-host machines; multi-host distributed networks require RDMA fallback.
GPU Direct RDMA
Extending zero-copy shared buffers to direct GPU-to-GPU peer memory access over PCIe fabric.