Neural network
A computational model composed of interconnected layers of weights that process numerical inputs through non-linear transformations to predict outputs based on learned patterns.
What it is
At the lowest level, a neural network is a series of matrix multiplications where each layer transforms input data into a higher-dimensional representation. During inference, these weights are static and the computation flow is feed-forward, moving data from input layers through hidden layers to a final output. Models can range from thousands to trillions of parameters, where each parameter is typically a 16-bit or 32-bit float. This density of calculations is exactly why GPU compute is the primary bottleneck for inference throughput.
Why it matters
Understanding neural networks shifts your focus from traditional conditional logic to probabilistic output management. Because these models are black boxes, you cannot debug them with standard step-through debuggers, requiring you to treat outputs as stochastic data needing validation. Ignoring their internal structure leads to unmanaged latency and memory overhead, as memory usage scales linearly with parameter count and context window length. You must account for these resource requirements when sizing infrastructure or choosing between model scales.
In practice
In production, you rarely interact with individual weights but instead configure hyperparameters like temperature or top-p to control output diversity. When monitoring performance, you might track variance in latent space representations or latency spikes caused by high parameter counts. If you need to adapt behavior, you likely adjust the model using techniques like LoRA or fine-tuning rather than modifying the core neural architecture.
The tradeoff
The primary tradeoff is between model depth, which enables more complex reasoning, and inference latency, which increases linearly with the number of operations per token.