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Ollama vs vLLM: Which AI Tool Is Right for Your Hardware?

Side-by-side comparison of local model support, GPU requirements, OpenRouter compatibility, pricing, and setup difficulty. Find which tool fits your workflow and hardware.

Ollama

The industry standard for running LLMs locally. Simple CLI, massive model library (100K+), OpenAI-compatible API on port 11434. Powers Open WebUI, Continue, and more.

vLLM

Production-grade LLM serving engine. PagedAttention for efficient KV cache, high throughput, multi-user API serving. For deployments, not single-user chat.

Feature comparison

FeatureOllamavLLM
Typelocal llm tool, developer toollocal llm tool, developer tool
Open sourceYesYes
Pricingopen-sourceopen-source
Platformsmacos, linux, windows, cli, dockerlinux, docker, cli
Local modelsYesYes
OpenRouterNoNo
OllamaYesNo
GPU neededFor local modelsYes
CPU-onlyYesNo
Setupeasyhard

Which should you choose?

Choose Ollama if

  • Running LLMs locally as a backend for other apps
  • Local API server for development (drop-in OpenAI replacement)
  • Quick model testing via CLI
  • You need local model support

Choose vLLM if

  • Production LLM serving with high concurrency
  • Multi-user API endpoints for LLM access
  • Long context scenarios (PagedAttention is memory-efficient)

Hardware requirements

Ollama

No GPU required — runs on CPU for small models (3B-8B) with sufficient system RAM. For 7B models, 8 GB VRAM recommended for usable speeds. Default context window is only 2K — increase it for coding agents.

vLLM

NVIDIA GPU with CUDA required. Linux recommended. Same model VRAM requirements as other backends. vLLM's PagedAttention is more memory-efficient for long contexts and high concurrency.

Full compatibility details

Ollama full guide → model recommendations, VRAM table, setup instructionsvLLM full guide → model recommendations, VRAM table, setup instructions

Frequently asked questions

Which is better for local models: Ollama or vLLM?
Ollama has better local model support — it connects to Ollama, LM Studio, and llama.cpp directly. vLLM also supports local models.
Do I need a GPU for Ollama vs vLLM?
Ollama: No GPU required — runs on CPU for small models (3B-8B) with sufficient system RAM. For 7B models, 8 GB VRAM recommended for usable speeds. Default context window is only 2K — increase it for coding agents. vLLM: NVIDIA GPU with CUDA required. Linux recommended. Same model VRAM requirements as other backends. vLLM's PagedAttention is more memory-efficient for long contexts and high concurrency.
Which is cheaper: Ollama or vLLM?
Both Ollama (open-source) and vLLM (open-source) have comparable pricing models.