Tutorials
Hands-on deployment guides for AI frameworks, tools, and pipelines on dedicated GPU servers. Set up PyTorch, TensorFlow, vLLM, and more from scratch — full root access on bare metal.
Zero-Downtime Model Swap in Production
Upgrading your LLM version should not take your API offline. Here is the pattern for swapping models with zero downtime on a dedicated GPU.
ZFS vs ext4 on a GPU Server for Model Storage
ZFS offers snapshots, checksums, and compression. ext4 is the fast default. For model weight storage on a dedicated GPU, which…
browser-use Self-Hosted Agent
browser-use gives an LLM a Chrome browser to navigate. Self-hosted on a GPU server it becomes a complete web automation…
Health Check Endpoints for an LLM API
Liveness and readiness probes for a self-hosted LLM API - what each should check and how to configure them for…
RAG Chunking Strategy – What Actually Works
Chunking decides retrieval quality more than the embedder does. Practical strategies that outperform the naive 512-token split.
Remote VS Code on a Dedicated GPU Server
VS Code Remote-SSH gives you a native editor experience while code and GPU execute on a dedicated server. The setup…
Caddy Reverse Proxy for Ollama Setup
Caddy automates TLS and has a dead-simple config format. For a small Ollama deployment, it is a lower-friction alternative to…
Hybrid Search – BM25 Plus Embeddings on a GPU Server
Hybrid search combines classical lexical matching with dense vector retrieval. The implementation pattern that actually works in production.
Rolling Model Upgrade on an Inference Server
Replace replicas one at a time with the new model version. Cheaper than blue-green when you have multiple GPUs in…
RVC Voice Cloning on a GPU Server
Retrieval-based Voice Conversion trains a voice model from ~15 minutes of audio and converts any speech to that voice. Self-hosted…
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