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Focused guides and comparisons on the topics engineers search for most — standalone explainers that complement the full 8-chapter guide.
vLLM vs SGLang vs TensorRT-LLM
The three leading open-source inference engines, compared on performance, ease of use, and hardware support — with real benchmark data and a decision framework.
Read guide →GPU Inference Explained
How GPUs actually run model inference — compute vs memory bandwidth, prefill vs decode, and why the bottleneck is almost never what you think.
Read guide →AI Inference Hardware Guide
The landscape of AI inference hardware — GPUs, TPUs, and dedicated inference chips — and how to compare them on the specs that actually matter.
Read guide →LLM Inference Acceleration: Complete Guide
The complete toolkit for making LLM inference faster and cheaper — quantization, speculative decoding, KV caching, batching, and parallelism — and when each one actually helps.
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