Ollama vs vLLM

Architecture comparison 2 projects Built from source-audited deep dives

Both serve open-weight models behind an OpenAI-compatible API, but for different jobs. Ollama is a Go single binary that runs models through llama.cpp builds per accelerator, plus an MLX path on Apple Silicon, aimed at running models locally; vLLM is a Python engine with C++, CUDA and Rust components, built around PagedAttention and continuous batching for high-throughput serving on GPU clusters.

Ollama vLLM

Ollama: Go · MIT
vLLM: Python · Apache-2.0
Updated: 2026-09-26

Which one should you pick?

Pick Ollama if…

You want to run models on a laptop or single machine with one install command
You need Apple Silicon, CPU-only or Vulkan GPUs
You want both OpenAI- and Anthropic-compatible endpoints

Pick vLLM if…

You serve many concurrent users and care about GPU throughput
You need tensor or pipeline parallelism across several GPUs or nodes
You run on NVIDIA, AMD, Intel XPU or Google TPU accelerators

Side by side

Attribute	Ollama	vLLM
Written in	GoCTypeScript	Python RustCuda
License	MIT	Apache-2.0
Frontend	React Vite Tailwind CSS	none
Backend & APIs	Go Gin C++	Python Rust PyTorch
Data & persistence	SQLite	none
Infrastructure & deploy	Docker	Docker Ray
Key decisions	Inference runs through llama.cpp, compiled per accelerator backend GPU discovery is implemented natively per platform in Go Apple Silicon gets a dedicated MLX execution path alongside llama.cpp	PagedAttention Memory Management Continuous Batching and Chunked Prefill Hybrid Python and Rust Architecture
Audited	ec3cc23 · 2026-09-17	f3aa88d · 2026-09-17

How they differ

Runtime

Ollama is a Go module: a Gin HTTP server, a Cobra CLI and a Bubble Tea terminal UI. Inference runs through llama.cpp’s server, built separately for CPU, CUDA 12 and 13, ROCm and Vulkan, with a separate MLX runner on Apple Silicon and GPU discovery written natively per platform. vLLM is Python orchestration over C++, CUDA and Rust: a scheduler that allocates KV cache in pages (PagedAttention), interleaves prompt prefill with generation (continuous batching and chunked prefill), and dispatches to CUTLASS, FlashAttention, FlashInfer and Triton kernels.

State

Ollama keeps model state on disk in content-addressed manifests with embedded SQLite, so there is no database to provision. vLLM keeps no application database: it loads weights from the Hugging Face Hub, ModelScope or local disk as safetensors and caches KV blocks for shared prompt prefixes.

Deployment

Ollama installs with one script on macOS and Linux (or PowerShell on Windows) and is also published as a Docker image; a desktop app wraps a React UI. vLLM is deployed as a GPU service: you size gpu_memory_utilization, spread large models with tensor and pipeline parallelism, and use its specialised Docker images per hardware target.

Architecture diagrams

Ollama Open SVG

vLLM Open SVG

Frequently asked questions

Are Ollama and vLLM OpenAI-compatible?

Yes. vLLM serves the chat completions, completions, embeddings and models endpoints with streaming. Ollama implements OpenAI Chat Completions and the Responses API, and also translates Anthropic-format requests.

Which one needs a GPU?

Ollama builds llama.cpp for CPU as well as CUDA, ROCm and Vulkan, and uses MLX on Apple Silicon, so it runs without a dedicated GPU. vLLM targets accelerators (CUDA, ROCm, Intel XPU, TPU) and is tuned to fill GPU memory with its KV cache.

What are the licenses?

Ollama is MIT and vLLM is Apache-2.0.

More architecture comparisons

View all