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Three tools dominate local LLM inference in 2026: Ollama for simplicity, LM Studio for GUI users, and vLLM for production serving. They solve different problems. Here’s when to use each.

Quick comparison

	Ollama	LM Studio	vLLM
Best for	Developers, CLI users	Beginners, GUI users	Production, multi-user
Interface	CLI + API	Desktop GUI + API	API only
Setup time	2 minutes	5 minutes	15 minutes
Model format	GGUF	GGUF	SafeTensors, GPTQ, AWQ
API compatible	OpenAI ✅	OpenAI ✅	OpenAI ✅
Multi-GPU	❌	❌	✅
Concurrent users	Basic	Basic	✅ Optimized
Continuous batching	❌	❌	✅
Prefix caching	❌	❌	✅
Throughput (concurrent)	1x baseline	~1x	16x (vs Ollama)
OS support	Mac, Linux, Windows	Mac, Linux, Windows	Linux (GPU required)
Price	Free	Free	Free

Ollama — the developer default

Ollama is the right choice for 80% of developers. One command to install, one command to run:

brew install ollama
ollama pull devstral-small:24b
ollama run devstral-small:24b

It exposes an OpenAI-compatible API on localhost:11434 that works with Aider, Continue.dev, OpenCode, and every other tool.

Choose Ollama when:

• You’re a solo developer
• You want the fastest setup
• You use CLI-based coding tools
• You’re on Mac (Apple Silicon runs great)

Don’t choose Ollama when:

• You need to serve 5+ concurrent users (throughput drops)
• You need multi-GPU inference
• You need maximum tokens/second for production

LM Studio — the GUI option

LM Studio provides a desktop app with a model browser, chat interface, and local API server. Download a model by clicking, not typing.

Choose LM Studio when:

• You prefer a graphical interface
• You want to browse and compare models visually
• You’re new to local LLMs
• You want a chat interface without setting up a frontend

Don’t choose LM Studio when:

• You need CLI automation
• You’re deploying to a server (no headless mode)
• You need production-grade serving

vLLM — production serving

vLLM is built for serving models to multiple users simultaneously. It uses continuous batching, prefix caching, and tensor parallelism to maximize throughput.

pip install vllm
vllm serve devstral-small-2506 --port 8000

Community benchmarks show vLLM delivers 16x more throughput than Ollama under concurrent load. For a team of developers sharing one GPU server, this is the difference between usable and unusable.

Choose vLLM when:

• You’re serving 5+ concurrent users
• You need maximum throughput
• You have multi-GPU hardware
• You’re building a production API

Don’t choose vLLM when:

• You’re a solo developer (overkill)
• You’re on Mac (limited support)
• You want the simplest setup

Performance comparison

Scenario	Ollama	LM Studio	vLLM
Single user, simple query	~30 tok/s	~30 tok/s	~35 tok/s
Single user, long context	~20 tok/s	~20 tok/s	~25 tok/s
5 concurrent users	~6 tok/s each	~6 tok/s each	~25 tok/s each
10 concurrent users	Unusable	Unusable	~20 tok/s each

Approximate, varies by hardware and model. Tested on RTX 4090 with Devstral Small 24B.

For solo use, all three perform similarly. The gap only appears under concurrent load.

The upgrade path

Most developers follow this progression:

1. Start with Ollama — learn local inference, test models
2. Stay with Ollama if you’re solo — it’s good enough
3. Upgrade to vLLM when you need to serve a team or build a production API
4. Add RunPod or Vultr GPU when your local hardware isn’t enough

See our free AI coding server guide for the complete local setup and GPU providers comparison for when you outgrow local hardware.

Model compatibility

Model	Ollama	LM Studio	vLLM
Devstral Small 24B	✅ GGUF	✅ GGUF	✅ SafeTensors
Qwen 3.5 27B	✅	✅	✅
DeepSeek R1 14B	✅	✅	✅
Gemma 4 12B	✅	✅	✅
Llama 4 Scout	✅	✅	✅

All three support the major open models. Ollama and LM Studio use GGUF (quantized, smaller). vLLM uses SafeTensors (full precision or GPTQ/AWQ quantization).

Related: Ollama Complete Guide · How to Serve LLMs with vLLM · Best AI Models for Mac · Free AI Coding Server · Best Cloud GPU Providers