How KV Caching Delivers 5x Faster LLM Inference: A Technical Breakdown
18 days ago 高效码农Deep Dive: How KV Caching Makes LLM Inference 5x Faster Every time you interact with ChatGPT, Claude, or any similar large language model (LLM), you likely notice a distinct pattern. The very first token—the initial fragment of the response—takes a noticeable moment to appear on your screen. However, once that first piece arrives, the rest of the text streams out almost instantly. This behavior is neither a user interface glitch nor a network delay. It is the result of a deliberate and critical engineering decision known as KV Caching (Key-Value Caching). This technique is fundamental to modern LLM infrastructure, capable …
Perplexity AI’s TransferEngine: Run Trillion-Parameter LLMs Across Any RDMA Hardware
3 months ago 高效码农Introduction: When LLM Scale Meets Network Bottlenecks Imagine trying to run a large language model with trillions of parameters, such as DeepSeek V3 (671 billion parameters) or Kimi K2 (1 trillion parameters). These models can no longer be fully deployed on a single 8-GPU server and must be distributed across multiple computing nodes. This reveals a surprising reality: the main constraint on performance is no longer computational power (FLOPs), but rather the efficiency of network communication between GPUs. This is the core challenge facing modern large language model systems. As model sizes explode, traditional collective communication libraries (like NCCL) struggle …