Making complex AI concepts understandable for humans
In LLM brochures, the "Context Window" is often simplified to a single number, such as 128K or 1M. However, for engineers, the context window is not merely "sto
During the inference process of Large Language Models (LLMs), the core bottleneck lies in their "autoregressive" nature: for every token generated, the entire m
In the evolution of Large Language Models (LLMs), a core contradiction has always persisted: we desire models with vast knowledge (requiring more parameters), y
In the field of LLM inference optimization, discussions often revolve around PagedAttention or Speculative Decoding. However, the underlying problem these techn
Many AI systems appear to run smoothly during demos: a request is sent, the model returns an answer, and the result appears on the page. However, once deployed
In production environments for Large Language Models (LLMs), one of the most significant components of inference cost is GPU utilization. If you observe a simpl
In the inference process of Large Language Models (LLMs), the most expensive resource is not computational power (FLOPs), but VRAM bandwidth and capacity. When
In production environments for Large Language Models (LLMs), the most intuitive pain point for users is the slow "typewriter" speed. Despite the astonishing com
Mixture of Experts (MoE) models appear to offer a straightforward optimization: only a small subset of experts is activated per request, allowing the parameter