Reasoning is the process of performing forward computation on new inputs and outputting results after model training is complete—it does not update weights, but only "uses" the learned parameters. For large models, reasoning typically involves two stages: first, parallel encoding of the entire prompt (prefill), followed by token-by-token autoregressive generation (decode). The resource characteristics of these two stages differ: prefill is compute-intensive, while decode is memory-access-intensive, thus requiring different optimization techniques.

The bottleneck in reasoning often lies not in compute power but in VRAM: each request must store a KV cache that grows continuously during generation. Poor management can lead to severe fragmentation and redundancy, limiting the number of concurrent requests. In 2023, Kwon et al. proposed the PagedAttention technique in the paper 《Efficient Memory Management for Large Language Model Serving with PagedAttention》. The inference framework vLLM, which incorporates this technique, improved throughput by 2–4 times under the same latency. VRAM management in inference systems is essentially an operating system problem. Additionally, quantization, knowledge distillation, speculative decoding, and continuous batching are common acceleration techniques. By drawing inspiration from the virtual memory and paging concepts of operating systems, the KV cache is divided into fixed-size blocks and mapped on demand, reducing waste to near zero and supporting sharing within requests and across requests.

From a cost structure perspective, training is a one-time investment, whereas inference incurs ongoing costs. The latency and throughput of inference directly determine whether large models can be deployed at scale. Such system optimizations are key to transforming "usable" into "affordable."

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