vLLM and Ollama are distinct tools for serving large language models, each optimized for different use cases. Ollama excels at simplicity for local, single-user interactions, making it easy to run models quickly on personal machines. In contrast, vLLM is designed for high-throughput production environments, capable of serving hundreds of concurrent users efficiently through advanced techniques like PagedAttention and continuous batching. Benchmarks show vLLM significantly outperforms Ollama under high concurrency, handling nearly 20 times more tokens per second with much lower latency. AI
IMPACT vLLM and Ollama serve different LLM deployment needs, with vLLM excelling in high-concurrency production and Ollama in local, simple use cases.
RANK_REASON Comparison of two distinct software tools for LLM serving.
- Apple Silicon
- graphics processing unit
- KV cache
- Llama 3.1 8B
- llama.cpp
- Mlx
- NVIDIA A100 40GB
- Ollama
- PagedAttention
- Sky Computing Lab
- University of California, Berkeley
- vLLM
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