Unverified50% confidenceSolutionExact time
使用独立的、更轻量的LLM作为后处理层清理主模型的冗余输出，是一种管道式（pipeline）双模型架构方案
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50%
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Long-term
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8/22/2026
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UnverifiedEngineering approaches to compressing LLM inference latency and reducing costs include model distillation, speculative decoding, KV Cache optimization, and model quantization.67% similarUnverifiedMeshLLM can split large model layers and distribute them to multiple machines for collaborative inference in a pipeline fashion (model parallelism).67% similarVerifiedLarge language models are divided into two major types: base models (Base LLM) and instruction-tuned models (Instruction-tuned LLM)67% similarUnverifiedLayer-by-layer streaming loading is not an entirely new concept; similar model offloading approaches have been widely applied in inference frameworks such as llama.cpp's mmap mechanism, DeepSpeed Infinity, and FlexGen.67% similarVerifiedLLM 推理分为预填充阶段（Prefill，计算密集型）和解码阶段（Decode，内存带宽密集型）两个阶段64% similar
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