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.
RTX 5060 Ti 16GB Whisper API Setup
Self-hosted Whisper API on Blackwell 16GB - Faster-Whisper server with OpenAI-compatible /audio/transcriptions.
Axolotl on a Dedicated GPU Server
Axolotl is the config-driven fine-tuning framework most production teams reach for. Here is how to set it up on a…
DeepSeek R1 Distill Qwen 32B Deployment
The distilled R1 in Qwen 32B is the practical reasoning model for dedicated GPU hosting. Here is the full deployment…
DPO Training on a Dedicated GPU Server
Direct Preference Optimisation aligns a model to preferred responses without reward model complexity. Here is the practical setup.
Fine-Tuning an Embedding Model on a Dedicated GPU
Fine-tuning a small embedding model on your own query-document pairs almost always beats the off-the-shelf model for domain retrieval.
Flash Attention 2 Setup on a GPU Server
Flash Attention 2 is the default memory-efficient attention kernel in 2026. Getting it installed correctly on a dedicated GPU avoids…
Full Fine-Tune of a 7B Model on RTX 6000 Pro
When LoRA is not enough, full parameter fine-tuning of a 7B model fits comfortably on a 96GB RTX 6000 Pro.…
llama.cpp n-gpu-layers Tuning for Mixed Inference
-ngl controls how many transformer layers live on the GPU. Picking the right number balances speed against VRAM - with…
llama.cpp Server Thread Tuning for Dedicated GPUs
llama.cpp exposes five thread-related knobs that interact in non-obvious ways. Getting them right doubles throughput on some dedicated configurations.
LoRA Fine-Tuning Mistral 7B on a Dedicated GPU
LoRA at FP16 works comfortably on a 24GB GPU for Mistral 7B - the fastest practical path to a fine-tuned…
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