vLLM vs Ollama 2026: 793 vs 41 TPS Performance Benchmark

A new benchmark comparison has emerged highlighting stark performance differences between two of the most popular local LLM inference frameworks. vLLM demonstrated 793 tokens per second (TPS) throughput compared to Ollama's 41 TPS—a nearly 19x performance gap that has significant implications for practitioners choosing inference solutions.

For those deploying large language models locally, throughput directly impacts real-world usability and cost efficiency. vLLM's superior performance stems from its advanced optimizations including paged attention, continuous batching, and GPU memory management designed specifically for high-throughput scenarios. This benchmark suggests vLLM is better suited for production deployments requiring concurrent request handling, while Ollama may be preferable for simpler single-user or prototyping scenarios.

The test results emphasize the importance of benchmarking inference frameworks against your specific hardware and workload patterns. Check the full benchmark details to understand the test conditions, model sizes, and hardware used, as results can vary significantly based on GPU type, model quantization level, and batch sizes.

Source: tech-insider.org · Relevance: 10/10