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.
Load Balancer in Front of vLLM – Patterns That Work
Two vLLM replicas need a load balancer. Picking the right algorithm and the right tool prevents uneven load, broken streaming, and failed health checks.
vLLM on ROCm: Setup Guide for AMD GPUs (MI300X, RX 7900 XTX)
End-to-end guide to running vLLM on AMD ROCm GPUs, with install steps, model compatibility and CUDA performance comparisons.
Sentence Transformers GPU Batch Tuning
sentence-transformers defaults to tiny batches. On a dedicated GPU, bigger batches deliver 5-10x throughput - and the right number depends…
SSH Port Forwarding Workflow for GPU Development
Forward ports from your dedicated GPU to your laptop over SSH - no VPN needed. The fastest way to access…
ControlNet Union Self-Hosted
One ControlNet model that handles canny, depth, pose, scribble, and more - much smaller memory footprint than stacking multiple single-purpose…
CrewAI Multi-Agent on a Dedicated GPU
CrewAI defines agents as roles with goals and tools. Pointed at a self-hosted LLM it becomes a practical framework for…
MeloTTS Deployment Guide
MeloTTS is a compact multilingual TTS that runs fast on almost any GPU - good voice quality and permissive licence.
Mixed Precision – BF16 vs FP16 for Training
BF16 is the right default on modern GPUs. FP16 is legacy. The difference matters for numerical stability in LLM training.
Multi-Query RAG on a Dedicated GPU
Generate multiple rewrites of the user's question, retrieve for each, fuse results. A cheap 5-15% recall lift over vanilla single-query…
MixedBread mxbai-embed-large on a GPU Server
MixedBread AI's mxbai-embed-large scores at the top of MTEB for English retrieval - worth considering as a BGE alternative.
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