BaseRT

BaseRT is a local LLM runtime for Apple Silicon. Install once, run supported models on your device, and connect a coding agent without using an external API.

Разработка ИИ Агентов

AI Инструменты Разработчика

Большие языковые модели

Посетить Сайт

What BaseRT is

BaseRT is a local LLM runtime from Base Compute for Apple Silicon. The homepage describes it as the fastest LLM runtime on Apple Silicon and shows a one-command install flow for running models on your own device.

Its core job is to serve and run open models locally, with an emphasis on keeping inference on-device for individual developers and for coding-agent workflows. The product page specifically shows a local model being served and then connected to a coding agent without sending data to an external API.

Base Compute’s research page frames BaseRT as a native Metal inference runtime for Apple Silicon, with technical reports focused on throughput gains over llama.cpp and MLX. The enterprise page extends that on-device approach to teams that want AI on hardware they already own.

Features

One-command installation

The homepage presents a single-command install flow using a shell installer, which keeps setup simple for local use.

Native Metal runtime

BaseRT is described as a native Metal inference runtime built for Apple Silicon, with technical reports explaining that it uses chip-specific kernel fusion and custom dispatch logic.

Local model serving for agents

The product page says BaseRT can serve a model locally and then be used by a coding agent on the same machine, keeping prompts and outputs on-device.

Broad model-family coverage

The homepage lists supported model families including Qwen3, Qwen3.5, Qwen3.6, Llama 3.1, Llama 3.2, Gemma 3, Gemma 4, Mistral, Phi-3, and Nomic BERT.

Multiple quantisation formats

The research page states support for a wide range of model families across eight quantisation formats from Q2 to FP16 on Apple M-series devices.

Supporting documentation and community channels

The site provides links to technical reports, docs, GitHub, and Discord, which gives users a path for deeper implementation details and community support.

Where BaseRT fits

Local coding-agent setup
Developers can run a coding agent against a model served on their own Mac, keeping the full loop local instead of routing prompts through a hosted API.
On-device model experimentation
Individuals who want to experiment with open models on Apple Silicon can install BaseRT quickly and run inference on their own machine.
Fleet planning for on-device AI
Engineering teams can use the enterprise workflow to evaluate how local AI fits into their existing laptop and workstation fleet before adopting it more broadly.
Private document workflows
Organizations handling sensitive material can keep AI-assisted drafting, summarization, or analysis on-device to avoid sending data to a third-party service.
Runtime evaluation and benchmarking
Teams focused on performance can use the research and benchmark material to compare BaseRT with llama.cpp and MLX on Apple hardware.

Pros and Cons

Pros

Designed for local inference, so models can run on the user’s own device instead of an external service.
The homepage provides a simple install command and a clear serve-and-connect workflow for coding agents.
The product site names specific supported model families, which makes the intended model scope more concrete than a generic runtime page.
Research pages and technical reports are available for readers who want implementation detail and performance context.
Community and support links are visible on the product site, including docs, GitHub, and Discord.

Cons

The public site does not provide a verified pricing page; the pricing URL currently returns a 404.
The strongest runtime details are documented for Apple Silicon, so support outside that platform is not clearly established on the source pages.
Supported workflows are clearly shown for local serving and coding agents, but broader integrations and API compatibility are not documented on the main product page.

FAQ

How do you set up BaseRT?

BaseRT is installed from the command line with a one-line install script shown on the product page. After installation, you can serve a supported model locally and point a coding agent at that server.

Who is BaseRT for?

The homepage highlights local coding-agent workflows and says BaseRT is for people building on-device AI with open source models. The enterprise page also positions it for organizations that want AI on laptops and workstations they already own.

What models can it run?

BaseRT supports local model serving on Apple Silicon, and the research page says it supports a wide range of model families across Apple M-series devices. The homepage lists Qwen3, Qwen3.5, Qwen3.6, Llama 3.1, Llama 3.2, Gemma 3, Gemma 4, Mistral, Phi-3, and Nomic BERT as supported models.

What does BaseRT cost?

The source does not document a public pricing page for BaseRT. The pricing URL currently returns a 404, so pricing and plan details are not verified from the available evidence.

Does BaseRT support non-Apple hardware?

The product pages focus on Apple Silicon and Apple M-series devices. The enterprise page mentions Windows and Linux workstations as general on-device AI hardware, but the BaseRT runtime itself is presented on the site as an Apple Silicon runtime.

Quick Facts

Category: Developer Tool / AI Infrastructure
Primary platform: Apple Silicon / Apple M-series devices
Core workflow: Install locally, serve a model, connect a coding agent on the same machine
Supported model families: Qwen3, Qwen3.5, Qwen3.6, Llama 3.1, Llama 3.2, Gemma 3, Gemma 4, Mistral, Phi-3, Nomic BERT
Source domain: basecompute.co
Company: Base Compute Pty. Ltd.

Аналитика BaseRT

Данные о трафике приведены только для справки.

Трафик и рейтинги

Посещений в месяц: 1,4 тыс.
Средняя длительность визита: 04:28
Страниц за визит: 2.00

Динамика трафика

Основные регионы

Аналитика трафика пока недоступна.

Источники трафика

Альтернативы BaseRT

AakarDev AI

AakarDev AI helps teams manage AI provider access, project setups, logs, and analytics in one dashboard with BYOK support.

Orca

Orca is an Agent Development Environment for shipping with coding agents, running multiple CLI agents in parallel across isolated worktrees, with desktop and mobile workflows.

AI Magicx

AI Magicx is a unified AI workspace for chat, image, video, voice, music, email and developer tasks, helping teams and creators manage multiple models in one place.

Paper

Paper is a design tool that connects canvas, code, and AI agents so teams can create, share, and ship work in one workflow. Includes desktop app and MCP access.

blop

blop is a QA agent that writes browser tests as code in your repo, runs them in CI, clusters repeated failures, and can open PRs to fix broken tests.

RLAMA

RLAMA is a local AI platform for building RAG systems and intelligent agents on macOS, Linux, and Windows, with HTTP API support.