In the realm of machine learning, performance benchmarks are crucial for understanding model efficiency. This article delves into a comparison of four prominent models: vLLM, Ollama, llama.cpp, and SGLang.
Recent tests indicate that Ollama experiences a significant drop in performance when subjected to heavy loads, collapsing to only 41 tokens. This finding raises questions about its scalability and practical applications.
The analysis not only highlights the weaknesses of Ollama but also provides insights into the strengths of its competitors, vLLM, llama.cpp, and SGLang, which maintain more stable performance under similar conditions.