OpenLLM allows developers to run any open-source LLMs (Llama 3.3, Qwen2.5, Phi3 and more) or custom models as OpenAI-compatible APIs with a single command. It features a built-in chat UI, state-of-the-art inference backends, and a simplified workflow for creating enterprise-grade cloud deployment with Docker, Kubernetes, and BentoCloud.

Aug 30, 2024 · In this guide, you’ll explore how to build a powerful and scalable local LLM environment, enabling you to harness the full potential of these advanced models.

Apr 1, 2025 · Running LLMs locally offers several compelling benefits: Privacy: Maintain complete control over your data, ensuring that sensitive information remains within your local environment and does not get transmitted to external servers.

Feb 8, 2025 · This article recommends a Linux server build that’s LLM-optimized for under $2,000 – a setup that rivals or beats pre-built solutions like Apple’s Mac Studio for cost and raw performance for LLM workloads.

Mar 12, 2024 · Setting up a port-forward to your local LLM server is a free solution for mobile access. There are many open-source tools for hosting open weights LLMs locally for inference, from the command...

Feb 3, 2025 · Therefore, setting up a local LLM server effectively is beneficial to save costs. In this post, I will discuss configuring a server to use multiple models, as illustrated in the above diagram....

Jul 30, 2023 · Despite the abundance of frameworks for LLMs inference, each serves its specific purpose. Here are some key points to consider: Use vLLM when maximum speed is required for batched prompt...

You can serve local LLMs from LM Studio's Developer tab, either on localhost or on the network. LM Studio's APIs can be used through an OpenAI compatibility mode, ehanced REST API, or through a client library like lmstudio-js.

In this blog post, we will take the first steps toward deploying an LLM on your own machine. This setup will serve as the foundation for exploring advanced techniques such as fine-tuning, quantization, and reinforcement learning. For this reason, we’ll start with a base model and a simple server, setting everything up from scratch.

LLM inference engines and servers are designed to optimize the memory usage and performance of LLMs in production. They help you achieve high throughput and low latency, ensuring your LLMs can handle a large number of requests and deliver responses quickly.