How to Run Large AI Models Remotely

The future of MCP is remote — and it's already here. If you’ve been playing with AI agents or LLM tools like Copilot in VS Code... You’ve probably heard of MCP (Model Context Protocol). It’s fast becoming the connective tissue for the modern AI stack. Now imagine this: → Instead of every tool running locally, → You run a remote MCP server — fully serverless. → Hosted on Azure Container Apps. → Secure, scalable, and API-key protected. That’s exactly what Anthony Chu did. ✅ Built a remote MCP server using FastAPI ✅ Added SSE transport support ✅ Protected with API key auth ✅ Deployed it to Azure Container Apps ✅ Hooked it up to VS Code and Copilot Best part? It just works. So if you’re: ☁️ Running AI agents 💬 Building tools for devs 🔐 Exploring secure remote access 🌍 Or want scalable inference endpoints... This guide will help you deploy your own remote MCP server in under an hour. 📖 Full write-up can be found here: https://lnkd.in/gHiBFHAz ♻️ Repost if you’re ready to take your MCP skills cloud-native. #MCP #Serverless #AzureContainerApps #GitHubCopilot #AIInfrastructure #FastAPI #LLM #OpenSource

DevOps and AI pros, securing data while running powerful AI models on Kubernetes is tough. I wanted to make a demo video that shows how to self-host OpenAI's gpt-oss-20b with vLLM and Open WebUI, giving you a secure, streamlined setup that slots right into your cluster without the tool sprawl. What the demo covers: * Data privacy first: Host gpt-oss-20b on your infrastructure, keeping sensitive data in-house for strict compliance. * Easy deployment: Run vLLM on Kubernetes with a single A100 GPU for fast inference, designed for DevOps workflows. * Clean interface: Open WebUI delivers an offline-ready UI for gpt-oss-20b with RAG support for custom AI tasks, no external services needed. * Resource savvy: vLLM's PagedAttention and MXFP4 quantization optimize AI workloads, keeping your cluster and costs lean. * Scales effortlessly: From on-prem GPUs to cloud EKS or GKE, SkyPilot and vLLM make private AI deployments simple. For teams wanting secure, self-hosted AI without the Kubernetes chaos, this is it. Watch the demo to see how vLLM and Open WebUI deliver private AI hosting that's robust, scalable, and keeps your data safe.

Running your own large language model at scale is hard. That's what AIBrix is built to solve. Serving an LLM to thousands of users without burning through GPUs is one of the toughest challenges in AI today. AIBrix is an open source, cloud-native toolkit that sits on top of inference engines like vLLM. Its purpose is simple: make large-scale model serving faster, cheaper, and more reliable. Here’s what it gives you: 1. Smart Gateway and Routing AIBrix acts like a traffic controller. Instead of sending requests blindly, it routes them to the best model instance based on load, cache, and GPU usage. That means lower latency and smoother multi-turn conversations. 2. High-Density LoRA Management If you’ve fine-tuned lots of LoRA variants, AIBrix helps load and unload them dynamically so you don’t waste GPU memory. 3. LLM-Aware Autoscaling Traditional autoscalers only look at request counts. AIBrix scales based on tokens and cache pressure, which matches the actual workload of a model. 4. Distributed KV Cache Pooling LLMs run faster when they can reuse past computations. AIBrix shares that cache across nodes, improving throughput by 50 percent or more while reducing latency. 5. Multi-Node Orchestration Very large models often need to span multiple machines. AIBrix coordinates that across Kubernetes and Ray so it just works. 6. GPU Mix and Cost Optimization Not all GPUs are created equal. AIBrix balances performance and cost by choosing the right hardware for each request. 7. Diagnostics and Failure Simulation It even includes tools to detect GPU issues early and test how your system handles failures before they happen in production. In other words, AIBrix turns LLM serving from a fragile DIY setup into a production-ready platform. If you’re looking at how to bring open source models like Qwen or LLaMA into production, this is the kind of backbone that makes the difference between “it runs” and “it scales.” https://lnkd.in/gRwb6qbc