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This report provides a comparative analysis of specialized large language model (LLM) inference frameworks designed to overcome hardware limitations and high computational costs. It distinguishes between high-throughput server solutions like vLLM, which uses PagedAttention to eliminate memory fragmentation, and SGLang, which optimizes complex, multi-turn interactions through RadixAttention and structured generation. For local deployment, the text evaluates Ollama and LM Studio, highlighting how they leverage llama.cpp and the GGUF format to run models on consumer-grade hardware. The study further explores critical performance-enhancing technologies such as quantization, speculative decoding, and continuous batching. Ultimately, the sources serve as a guide for selecting the right infrastructure based on specific needs, ranging from cloud-scale API services to private local assistants.

Comparative Analysis of Large Model Inference Optimization Frameworks