Microsoft

Microsoft Phi-4

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

Laptop running a small language model locally

Overview

Phi-4 is a 14-billion-parameter open-weight model released under the MIT licence and trained on a mix of synthetic, filtered public and academic data with an emphasis on reasoning quality. Its size makes it a practical candidate for local deployment on workstation-class hardware.

Typical use cases

  • Local reasoning assistants
  • Private summarisation
  • Structured extraction

Deployment environment

Workstation, edge server or capable AI PC

Key specifications

Parameters
14 billion
Licence
MIT
Design goal
Low-latency scenarios with strong reasoning quality
Training data
Synthetic, filtered public and academic sources

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

Ollama

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

Type
Local model runtime and server
Licence
MIT
Interfaces
Command line and local HTTP API
Deployment
Workstation, edge server or on-premise host
Private assistants
Offline document analysis
Prototyping local inference
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