Catching a vLLM Latency Spike with eBPF and an Open-Weight LLM

TL;DR: A vLLM latency spike was debugged using a fully open source stack: eBPF kernel tracing + MiniMax M2.7 (open-weight model via Ollama) + MCP (open protocol). The AI autonomously called 4 tools, identified the root cause in under a minute, and dug into call stacks to find the specific vLLM kernel functions involved. No proprietary APIs, no vendor lock-in. Why This Matters Most GPU debugging demos use Claude or GPT-4. That creates a dependency: the observability workflow requires a paid API key and sends production trace data to a third-party cloud. We wanted to prove this works with a fully open source stack - open model, open tracing agent, open protocol. Can the same investigation run with open-weight models instead of proprietary APIs? That is what we tested. Ingero's MCP server speaks the Model Context Protocol - a standard interface that works with any AI, not just one vendor. We connected it to MiniMax M2.7 via Ollama and ollmcp (a terminal MCP client for Ollama models) and a