vLLM vs Ollama
Two of the top model serving, side by side: score, setup, license, activity and what each review found.
vLLM
High-throughput LLM serving engine with OpenAI and Anthropic APIs
Ollama
Runs open-weight models locally behind a CLI and REST API
| What we compare | vLLM | Ollama |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 91, widely used | 98, widely used |
| Freshness | 100, active | 100, active |
| Maintenance | 81, healthy | 82, healthy |
| Easy to run | 50, easy | 33, some setup |
| Agent-ready | 45, minimal | 70, partly |
| Facts from GitHub and the README | ||
| Stars | 93.6k | 182.7k |
| License | Apache-2.0 (permissive) | MIT (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Oct 2026 | Oct 2026 |
| Language | Not stated | Not stated |
| Docker | Yes | Yes |
| GPU | Optional | Optional |
| arm64 or Apple Silicon | Mentioned | Not stated |
vLLM
vLLM is a Python serving engine for Hugging Face models that batches requests continuously with PagedAttention, prefix caching and speculative decoding, exposing an OpenAI-compatible API plus Anthropic Messages API and gRPC. It covers 200+ architectures (dense, MoE, multimodal, embedding) with FP8, INT8, GPTQ, AWQ and GGUF quantization and tensor, pipeline and expert parallelism.
Who it is for: teams serving open models to many concurrent users
Strengths
- Continuous batching with PagedAttention for high multi-user throughput
- 200+ Hugging Face architectures including MoE, multimodal and embedding models
- OpenAI, Anthropic Messages and gRPC endpoints with tool calling and structured output
- Runs on NVIDIA, AMD, Intel GPUs, CPUs, TPUs, Gaudi, Ascend via plugins
Weaknesses
- No web UI; API server only
- README gives no VRAM, port or model-size guidance
- Heavy Python, PyTorch and CUDA dependency chain; no single binary
- Most optimized kernels target NVIDIA and AMD GPUs; CPU path is secondary
- GPU optional
- Docker
- Models: 200+ Hugging Face architectures: Llama, Qwen, Gemma, Mixtral, DeepSeek-V3, GPT-OSS, LLaVA, Qwen-VL, E5-Mistral
Ollama
Ollama runs open-weight models locally with a CLI and a REST API on port 11434, pulling models from its own library (for example gemma4) and using llama.cpp as the inference backend. Install scripts cover macOS, Windows and Linux, and an official Docker image exists. The ollama launch command wires it into coding agents such as Claude Code, Codex, Copilot CLI and OpenCode, or into OpenClaw as a chat assistant.
Who it is for: anyone wanting local models behind a simple API
Strengths
- One command pulls and runs a model; REST API on 11434
- Official Docker image plus Python and JavaScript libraries
- ollama launch integrates with Claude Code, Codex, Copilot CLI, OpenCode
- Broad ecosystem: dozens of web, desktop and IDE clients listed
Weaknesses
- Single inference backend: llama.cpp
- Install is a curl piped to sh script
- README gives no RAM or VRAM guidance per model size
- Models come from Ollama's own registry; others need import steps
- GPU optional
- Docker
- Models: Ollama library models (e.g. gemma4), GGUF via llama.cpp
- port 11434