Show HN: ZSE – Open-source LLM inference engine with 3.9s cold starts (github.com)

ZSE has launched an open-source large language model (LLM) inference engine that significantly enhances efficiency and performance while keeping memory usage low. This engine features an innovative component called the Intelligence Orchestrator, which optimizes LLM usage based on available memory, allowing it to run models like Qwen and Llama with notable cold-start times of just 3.9 seconds for a 7B parameter model and 21.4 seconds for a 32B model. Key technical advancements include custom CUDA kernels for attention mechanisms, mixed precision quantization, and smarter cache management, leading to up to 70% memory savings. This development is significant for the AI/ML community as it addresses the growing demand for efficient AI model deployment, especially in resource-constrained environments like consumer-grade GPUs. With support for multiple model formats and an OpenAI-compatible API, ZSE opens avenues for researchers and developers to integrate advanced language models into applications without the usual hardware limitations. The introduction of efficiency modes allows users to choose between speed, balanced, memory-efficient, and ultra modes, making it versatile for varied deployment scenarios, from academic research to commercial applications.