vLLM vs Ollama: Which Should You Use in 2026?

vLLM achieves 793 tokens/second on an A100. Ollama achieves 41 tokens/second on the same hardware. That headline number is real — but it tells you almost nothing about which runtime you should actually use. The answer depends entirely on whether you're serving one user or a hundred, whether you need maximum simplicity or maximum throughput, and whether you're running on a developer laptop or a production GPU cluster.

What Each Runtime Is Designed For

Ollama was built for individual developers running models locally. Its design priorities are simplicity, portability, and zero-configuration GPU detection. You install it with one command, pull a model, and have a working OpenAI-compatible API in under five minutes. vLLM was built for production serving at scale, created by researchers at UC Berkeley. Its core innovation is PagedAttention — a memory management technique that handles the KV cache like virtual memory in an OS, allowing it to serve many concurrent requests without wasting VRAM.

Performance: The Real Numbers

The throughput gap between vLLM and Ollama is real but context-dependent. In Red Hat's benchmarks, vLLM achieves 793 tokens/second versus Ollama's 41 TPS on an A100 80 GB — a 19x difference. However, this measures throughput under concurrent load (many simultaneous requests). For a single user making sequential requests, the gap narrows dramatically: both runtimes achieve similar per-request latency for the first token. The throughput advantage of vLLM only materializes when multiple users are querying simultaneously.

Setup Complexity

Ollama installs in one command and works immediately on Mac, Linux, and Windows. vLLM requires Python 3.9+, CUDA 11.8+, and runs only on Linux with NVIDIA GPUs (or AMD with ROCm). There is no macOS support for vLLM. This is the most important practical difference for most developers: Ollama works everywhere, vLLM works on Linux+NVIDIA only.

# Ollama — one command, any platform
curl -fsSL https://ollama.com/install.sh | sh
ollama run qwen3.5:9b

# vLLM — Linux + NVIDIA only (v0.18+)
# Recommended install (handles torch backend automatically):
uv pip install vllm --torch-backend=auto
# Or: pip install vllm
# New server command (python -m vllm.entrypoints.openai.api_server deprecated in v0.18+):
vllm serve Qwen/Qwen3.5-9B --port 8000

When to Use Ollama

Ollama is the right choice when: you're a solo developer or small team (under 5 concurrent users), you need cross-platform support (Mac, Windows, or Linux), you want the fastest possible setup with zero configuration, you're running quantized models (GGUF format) on consumer hardware, or you want a model management CLI with pull/list/rm commands. The vast majority of local LLM use cases fall into this category.

When to Use vLLM

vLLM is the right choice when: you're serving 10+ concurrent users, you need maximum throughput for batch processing or API services, you're running on dedicated Linux+NVIDIA infrastructure (RunPod, Hetzner, DigitalOcean GPU droplets), you need continuous batching for production workloads, or you're running full-precision (fp16/bf16) models rather than quantized versions. vLLM is the standard choice for production AI API services.

Can You Use Both?

Yes — and many teams do. A common pattern is to use Ollama for local development (fast iteration, works on your laptop) and vLLM for production serving (maximum throughput, deployed on RunPod or Hetzner). Since both expose an OpenAI-compatible API, switching between them requires only a base URL change in your application code. LiteLLM can act as a proxy layer that routes requests to either runtime based on model name, making the switch transparent to your application.