Local AI in 2026: Ollama, vLLM, Docker Model Runner, and When to Use Each

Running models on your own hardware stopped being a hobbyist curiosity somewhere around 2024 and is now a mainstream engineering choice. Privacy rules, per-token cost, latency, and offline requirements all push teams toward local inference. But "local AI" spans three very different tools - Ollama, vLLM, and Docker Model Runner and picking the wrong one wastes weeks. This guide compares them honestly: what each is for, where each falls down, and a decision framework you can apply on Monday.

The three tools at a glance

They are not really competitors so much as tools for different jobs. Confusing them is the root of most bad local-AI decisions.

Ollama              ▸ Single-machine, dev-friendly, "brew install and go"
                      Best for: laptops, prototypes, single-user tools

vLLM                ▸ GPU inference server built for throughput & concurrency
                      Best for: production serving, many concurrent users

Docker Model Runner ▸ Run models as OCI artifacts in your container workflow
                      Best for: teams standardised on Docker/Kubernetes

Ollama: the developer laptop default

Ollama is the easiest on-ramp to local models. One install, one command, and you have a chat-capable model and an OpenAI-compatible endpoint on localhost. It handles model downloads, quantisation formats, and memory management for you, and runs happily on a MacBook's unified memory or a modest GPU.

# Install, pull a model, and chat — three lines.
curl -fsSL https://ollama.com/install.sh | sh
ollama pull llama3.3
ollama run llama3.3

# It also serves an OpenAI-compatible API on localhost:11434
curl http://localhost:11434/v1/chat/completions \
  -d '{"model":"llama3.3","messages":[{"role":"user","content":"hi"}]}'

vLLM: production throughput

vLLM is a serving engine engineered for one thing: getting the most tokens per second out of your GPUs while handling many concurrent requests. Its headline techniques — continuous batching and paged attention (PagedAttention) — let it pack far more simultaneous requests onto a GPU than naive serving, which is exactly what you need behind a real API.

pip install vllm

# Serve a model with an OpenAI-compatible endpoint, tuned for throughput.
vllm serve meta-llama/Llama-3.3-70B-Instruct \
  --tensor-parallel-size 2 \
  --max-model-len 8192 \
  --gpu-memory-utilization 0.90

Docker Model Runner: container-native models

Docker Model Runner treats models as first-class OCI artifacts — you pull and run a model much like a container image, and it integrates with the Docker tooling and Compose workflows your team already uses. For organisations standardised on containers, this means models live in the same registry, versioning, and deployment pipeline as everything else, which is a real operational win.

# Models as OCI artifacts, managed through the Docker workflow.
docker model pull ai/llama3.3
docker model run ai/llama3.3 "Explain paged attention in one sentence."

# Wire it into Compose alongside your app services.
docker model list

The decision framework

Strip it down to the question each tool answers best:

Sizing hardware without tears

The single most common local-AI mistake is underestimating VRAM. A rough rule: a model needs roughly its parameter count in bytes times the precision, plus headroom for the KV cache that grows with context length and concurrency. A quantised (4-bit) model is far more forgiving than full precision, which is why quantisation is the norm for local serving.

The bottom line

Local AI in 2026 is a solved problem at every scale — you just have to match the tool to the job. Reach for Ollama to explore and prototype, vLLM to serve at scale, and Docker Model Runner to keep models inside your container workflow. Decide honestly whether you even need local inference, size your VRAM for real load, and benchmark with your own traffic. Do that and you get the privacy, cost control, and latency that pushed you toward local in the first place.