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.
vLLM Structured Output and Guided Decoding
Force the model to emit valid JSON, a regex, or a choice from a set. vLLM supports three backends with different tradeoffs.
Dual RTX 5090 Llama 3 70B Deployment – Tensor Parallel Setup
A walkthrough of standing up a production-grade Llama 3 70B inference server on two RTX 5090s with tensor parallelism.
LoRAX Multi-LoRA Serving on a Dedicated GPU
One base model, many LoRA adapters, one GPU - how to serve dozens of fine-tuned variants without running dozens of…
Scaling vLLM Across Two GPUs – What Actually Changes
Moving from single-GPU vLLM to two-GPU tensor parallel changes throughput, latency, memory layout, and a few knobs you will not…
TGI Max Batch Prefill Tokens Tuning
Hugging Face Text Generation Inference's prefill batching parameter is the single most impactful knob you can tune for long-prompt workloads.
Triton Inference Server Configuration for GPU Workloads
Nvidia's Triton Inference Server serves more than LLMs - vision, audio, ensembles. Configuring it correctly on a dedicated GPU is…
vLLM Chunked Prefill Configuration
Chunked prefill keeps decode latency stable when big prompts arrive during active serving - the right config for mixed workloads.
vLLM Continuous Batching Tuning Guide
The three knobs that actually move vLLM throughput, how to measure their effect, and a tuning recipe for common workloads.
vLLM KV Cache Block Size Tuning
PagedAttention's block size controls memory fragmentation and throughput - the defaults are usually fine but not always.
vLLM Prefix Caching Performance Gains
Prefix caching reuses KV cache for repeated prompt prefixes. For RAG and few-shot workloads the speed-up is dramatic.
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