Gpu-Optimization

Abstract visualization of a tensor grid being mapped from float32 values into discrete quantization bins.

Why Quantization Works: The Mathematics That Makes LLMs 4x Smaller

Quantization shrinks 70B-parameter models from 140GB to 35GB without retraining. Here is the math, the failure modes, and why it works in production.

Abstract visualization of a GPU pipeline routing inference requests across multiple LLM instances.

Serving LLMs in Production: A Practical Guide to Latency, Throughput, and Cost

A production-focused walkthrough of how to serve large language models efficiently, covering batching strategies, quantization, KV cache management, and the observability you actually need.

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