Ollama’s simplicity has made it popular for local LLMs, but it hides a critical security flaw. Tens of thousands of Ollama instances are exposed to the public internet without authentication, allowing anyone to hijack GPU resources and even compromise the server. This article uses real-world data to reveal the scale of the problem, details the severe risks, and provides essential steps to secure your instance immediately.

This article introduces how to configure and use llm-api-benchmark-mcp-server, a tool that allows LLM Agents to measure LLM API throughput performance under natural language instructions, and details the steps for setting up and conducting concurrent performance tests in Roo Code.

This article provides a quick start guide for Linux and Windows platforms, including command examples for downloading, configuring, and running the tool.

This article compares the performance differences of vllm when using xformers and flash attention 2 as the backend attention mechanism.

This post documents how to run LLMs on a 1C1G VPS using Ollama.

This article compares the throughput of three large language model inference engines, VLLM, SGLang, and LMDeploy, in a short-input, long-output scenario. The unit of measurement is output tokens per second.