Enterprise Infrastructure Guide: Running Local LLMs for 70-150 Developers

A software startup engineer posed a practical question about scaling local LLM deployment across 70-150 developers using agentic coding workflows for code generation, refactoring, test writing, and PR reviews. This real-world scenario reflects the growing adoption of self-hosted LLMs in professional development environments where latency, privacy, and cost control are critical factors.

The discussion addresses key infrastructure decisions: whether to use centralized inference servers (vLLM, TGI) versus distributed edge deployment, quantisation strategies for balancing performance and memory footprint, GPU allocation and load balancing, and integration with development tools. These are precisely the challenges organizations face when moving beyond prototypes to production local LLM systems.

For practitioners building similar systems, this thread likely contains valuable community recommendations on frameworks (Ollama, llama.cpp, vLLM), hardware provisioning, and architectural patterns proven at modest but meaningful scale. Enterprise-grade local LLM deployment is no longer theoretical—this discussion captures real implementation constraints and solutions.

Source: r/LocalLLaMA · Relevance: 8/10