Ollama

Ollama

MIT-licensed runtime for pulling and serving open-weight models locally through a CLI and local API.

Terminal window showing a locally hosted language model responding on a dark screen

Overview

Ollama packages model download, quantised storage and serving behind a single command line and local HTTP API, with a Go-based client and server architecture. It supports open-weight families including Llama, Gemma, Qwen, Mistral, DeepSeek and gpt-oss.

Typical use cases

  • Private assistants
  • Offline document analysis
  • Prototyping local inference

Deployment environment

Workstation, edge server or on-premise host

Key specifications

Type
Local model runtime and server
Licence
MIT
Interfaces
Command line and local HTTP API
Model families
Llama, Gemma, Qwen, Mistral, DeepSeek, gpt-oss and others
Implementation
Written primarily in Go

Specifications taken from current manufacturer documentation and last checked on 2026-09-08.

Source: manufacturer documentation

Specifications are summarised from publicly published manufacturer material and are provided for orientation only. Always confirm current figures directly with the manufacturer before purchasing or designing in.

Terminal showing token generation from a local model
ggml.ai (llama.cpp)

llama.cpp

MIT-licensed C and C++ inference engine for open-weight language models, on CPU or GPU backends.

Type
LLM inference engine in C and C++
Licence
MIT
Backends
CPU plus CUDA, Metal and Vulkan
Deployment
Edge board, workstation or server, with or without a GPU
On-device assistants
Quantised model serving
Arm and Apple silicon inference
Workstation running a local language model in a dim office
Mistral AI

Mistral Small 3

Apache 2.0 licensed 24-billion-parameter model built for low latency and self-hosted deployment.

Parameters
24 billion
Licence
Apache 2.0
Design goal
Latency-optimised, positioned against Llama 3.3 70B and Qwen 32B class models
Deployment
Workstation or edge server with sufficient memory
Self-hosted assistants
Document understanding
On-premise agents
Laptop running a small language model locally
Microsoft

Microsoft Phi-4

MIT-licensed 14-billion-parameter small language model built for low-latency reasoning tasks.

Parameters
14 billion
Licence
MIT
Design goal
Low-latency scenarios with strong reasoning quality
Deployment
Workstation, edge server or capable AI PC
Local reasoning assistants
Private summarisation
Structured extraction