Scaling self-hosted LLMs across multiple machines requires choosing between peer-to-peer federated networks and supervisor-worker topologies. While tools like exo offer zero-config discovery for high-speed hardware links, and GPUStack unifies management across multi-vendor accelerators, network latency remains a critical bottleneck.
Without prefix-cache-aware routing, multi-turn prompts trigger redundant compute across nodes, driving up generation times. Evaluating physical interconnect speeds and KV-cache steering is necessary before moving beyond single-node inference deployments.
Read the full article: Evaluating Multi-Node LLM Orchestrators: Exo, GPUStack, and LocalAI
Top comments (0)