Ollama

About

Ollama is the fastest way to run large language models on your own hardware. One command, no cloud dependency, no API keys, no per-token billing. You download a model, you run it. That simplicity made it the most popular local AI tool on GitHub with 167,000+ stars. Version 0.19, released March 31, 2026, changes the performance equation on Mac. Ollama now integrates Apple's MLX framework, leveraging the unified memory architecture on Apple Silicon chips. The result: prefill speed jumped from 1,154 to 1,810 tokens per second. Decode speed nearly doubled from 58 to 112 tokens per second. On M5 chips with Neural Accelerators, performance climbs even higher, hitting 1,851 tokens per second prefill and 134 tokens per second decode with int4 quantization. That is a 93% improvement in decode speed. For context, decode speed determines how fast the model generates responses. Doubling it means the difference between a noticeable wait and an instant reply. The model library is massive: Qwen, Gemma, DeepSeek, Llama, Mistral, and dozens more. Run ollama run qwen3.5 and you are chatting with a 32B parameter model in your terminal. No signup. No cloud. No data leaving your machine. Monthly downloads grew from 100K in Q1 2023 to 52 million in Q1 2026. That is 520x growth in three years. Ollama is not a niche tool anymore. It is the default way developers run local AI. The main limitation: you need hardware. The MLX preview requires 32GB+ unified memory. Smaller models run on less, but the best experience demands a recent Mac with serious RAM. On Linux and Windows, GPU offloading to NVIDIA or AMD cards is supported but MLX is Mac-only. If you are building AI-powered applications locally, pair Ollama with specialized models like TimesFM for domain-specific tasks. For cloud AI alternatives, check our AI coding tools directory.

Key Features

• One-command model download and execution: ollama run <model>
• Apple MLX integration: 93% faster decode on Apple Silicon (v0.19)
• M5 Neural Accelerator support: 1,851 tok/s prefill, 134 tok/s decode
• 167K+ GitHub stars, 52M monthly downloads
• Supports Qwen, Gemma, DeepSeek, Llama, Mistral, and dozens more
• REST API for integration into applications and workflows
• GPU offloading on NVIDIA and AMD (Linux/Windows)
• Unified memory architecture leverage on Apple Silicon
• Model customization via Modelfiles
• Docker support for containerized deployments

Use Cases

• 1Running LLMs locally for privacy-sensitive applications without cloud dependency
• 2Developers building AI features with zero per-token costs
• 3Prototyping AI applications before committing to cloud API pricing
• 4Enterprise teams running models on-premise for compliance requirements
• 5Apple Silicon Mac users who want maximum local inference speed