Introducing LazyLLM for efficient long context LLM Inference
LazyLLM is a novel method designed to accelerate the inference of transformer-based large language models (LLMs). This technique focuses on improving the time-to-first-token (TTFT) during the inference process, which can be a bottleneck for long prompts. Key Features of LazyLLM: Dynamic Pruning: Selectively computes the key-value (KV) cache for important tokens, reducing unnecessary computations. Seamless […]