{"id":152545,"date":"2026-11-09T08:36:25","date_gmt":"2026-11-09T08:36:25","guid":{"rendered":"https:\/\/www.hostinger.com\/ca\/tutorials\/vllm-vs-ollama\/"},"modified":"2026-11-09T09:04:17","modified_gmt":"2026-11-09T09:04:17","slug":"vllm-vs-ollama","status":"publish","type":"post","link":"\/ca\/tutorials\/vllm-vs-ollama\/","title":{"rendered":"vLLM vs. Ollama: Which one should you use for LLM inference?"},"content":{"rendered":"

Choose Ollama for simpler local development and model management, and vLLM for high-throughput inference when your app needs to handle several requests at once.<\/p>

Comparing vLLM vs. Ollama, the key difference between them is what each tool prioritizes: vLLM focuses on serving models to apps and controlling how requests use GPU resources, while Ollama makes it easier to download, switch, and run models locally.<\/p>

Aspect<\/strong><\/td>vLLM<\/strong><\/td>Ollama<\/strong><\/td><\/tr>
Main purpose<\/td>Serve models to apps<\/td>Download, manage, and run models<\/td><\/tr>
Performance at 8 concurrent requests<\/td>371.63 tokens\/s; 0.19 s median wait for first text<\/td>10.86 tokens\/s; 143.6 s median wait for first text<\/td><\/tr>
Architecture<\/td>Shared inference engine schedules requests and manages GPU memory<\/td>Server loads models and manages separate model runners<\/td><\/tr>
Model compatibility<\/td>Supported models from Hugging Face or a local path<\/td>Models from Ollama’s library or compatible local files, including Safetensors and GGUF<\/td><\/tr>
Ease of use<\/td>Requires more serving and deployment configuration<\/td>Simpler workflow for downloading, switching, and running models<\/td><\/tr>
Concurrent requests<\/td>Serve multiple requests at once and control how many run concurrently<\/td>Run multiple requests in parallel per model; extra requests wait in a queue when capacity is full<\/td><\/tr>
GPU support<\/td>NVIDIA GPUs with compute capability 7.5+<\/strong>, supported AMD GPUs, and separate Apple Silicon support<\/td>NVIDIA GPUs with compute capability 5.0+<\/strong>, supported AMD GPUs, and native Apple Silicon support<\/td><\/tr>
Multi-GPU<\/td>Split model execution across multiple GPUs or servers<\/td>Use one GPU when the model fits; otherwise, spread the model across available GPUs<\/td><\/tr><\/tbody><\/table><\/figure>

We ran vLLM and Ollama on matching NVIDIA L40S GPU instances using the same large language model (LLM), Qwen2.5-Coder-7B-Instruct, at 16-bit floating-point (FP16) precision.<\/p>

At eight simultaneous requests, vLLM produced over 34 times as much output per second, while its median wait for the first text was over 770 times shorter.<\/p>

<\/p>

How do vLLM and Ollama work?<\/h2>

vLLM works through a shared inference engine that coordinates requests and GPU resources, while Ollama simplifies model management<\/a> through a server that loads models and manages their runners for you.<\/p>

vLLM’s scheduler and memory components decide which requests run together and how GPU memory is used. Ollama’s server starts or reuses a model runner as needed and keeps models loaded when memory allows.<\/p>

How does vLLM serve models?<\/h3>

vLLM serves models by receiving requests through an API, deciding which ones to process, and returning generated tokens to your app. Its scheduler decides which requests run together based on the available GPU memory and processing capacity.<\/p>

To generate a new token, the model needs information it calculated from the tokens that came before it. vLLM keeps this information in the key-value cache (KV cache), so the model doesn’t have to calculate it again for every new token.<\/p>

PagedAttention, vLLM’s method for organizing the KV cache, splits that memory into small, fixed-size blocks.<\/p>

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