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BaseRT

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Fast open-source LLM inference for Apple Silicon, on-device

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Tracked since2026
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The Bottom Line

Entry price

Free, no paid tier

Biggest pro

Fast inference speeds, especially on prefill and decode phases.

Biggest con

Only compatible with Apple Silicon (M-series) hardware.

TL;DR - BaseRT

  • Optimized runtime for Apple Silicon delivering significant speedups compared to alternatives.
  • Enables local model serving for coding agents, ensuring data privacy by keeping everything on the user's machine.
  • Open-source project with community support via Discord and GitHub, supporting multiple popular model families.
Pricing: Free forever
Best for: Individuals & startups

What is BaseRT?

Editorial review
BaseRT is a high-performance runtime designed specifically for Apple Silicon, enabling fast inference of open-source language models. It delivers significant speed improvements over MLX and llama.cpp, with notable speedups in benchmarks. BaseRT allows developers to serve models locally and use them with coding agents, keeping all data on-device without requiring API keys or internet connectivity. It supports a range of popular models including Qwen, Llama, Gemma, Mistral, Phi, and Nomic BERT.
how we evaluateSourcebasecompute.co ↗

Pros & Cons

Pros

  • Fast inference speeds, especially on prefill and decode phases.
  • Keeps data fully local, enhancing privacy and security for sensitive work.
  • Free and open-source, with active community support.

Cons

  • Only compatible with Apple Silicon (M-series) hardware.
  • Model support may not cover all open-source models available in other runtimes.

Key Features

Supports a wide range of models: Qwen, Llama, Gemma, Mistral, Phi, Nomic BERT, and more.Serves models via a simple `basert serve` command for integration with coding agents.Installs with a single curl command for quick setup.Delivers significantly faster prefill tokens per second compared to MLX and llama.cpp.Community-driven development with documentation, GitHub repository, and Discord chat.

Pricing Plans

Pricing checked Aug 19, 2026

BaseRT

Free

  • The fastest LLM runtime on Apple Silicon
  • Prefill up to 6.4x faster than llama.cpp and 3.9x faster than MLX
  • Decode up to 33% faster than MLX
Calculate your costView full pricing

Is BaseRT worth the price?

100/100

BaseRT is completely free at $0/month, making it an exceptionally generous offering for Apple Silicon users.

It outperforms leading open-source runtimes like llama.cpp and MLX on prefill and decode speeds, so there is no cost barrier to adopting it. This is best for developers and researchers who want maximum inference performance on Apple hardware without spending anything.

Hidden Costs & Gotchas

Requires Apple Silicon hardware (no x86/cloud)

No official enterprise support or SLA

No guaranteed update frequency or roadmap

May lack features of paid runtimes (e.g., quantization options)

How BaseRT Compares to Competitors

BaseRT competes directly with free open-source runtimes like llama.cpp and MLX, but claims up to 6.4x faster prefill and 33% faster decode. Since both competitors are also free, BaseRT's value is purely in its performance advantage, not in pricing. It is cheaper than any proprietary API or cloud inference service, but requires local Apple Silicon hardware.

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Best BaseRT Alternatives

Top alternatives based on features, pricing, and user needs.

GPT4All logo
GPT4AllFree

Run local LLMs on consumer hardware

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LM Studio logo
LM StudioFree

Run local LLMs with a beautiful interface

Ollama logo
OllamaFree

Run open-source LLMs locally with one command

Llama.cpp logo
Llama.cppFree

Run LLMs efficiently on consumer hardware

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Still deciding?

Most buyers shortlist 2 or 3 tools before committing. Pull a side-by-side comparison or browse the full alternatives shortlist below.

BaseRT vs GPT4AllHead-to-head: features, pricing, who winsBaseRT vs LM StudioHead-to-head: features, pricing, who winsBaseRT vs OllamaHead-to-head: features, pricing, who wins

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BaseRT FAQ

How does BaseRT improve inference speed for Apple Silicon users?

BaseRT delivers significant speed improvements over MLX and llama.cpp, with notable speedups in both prefill and decode phases. This makes it a high-performance runtime for running open-source language models locally on Apple Silicon hardware.

Which teams benefit most from using BaseRT for local model inference?

Teams working on privacy-sensitive projects, such as those handling proprietary code or confidential data, benefit most because BaseRT keeps all data on-device without requiring API keys or internet connectivity. It is also well-suited for developers who need fast, local inference for coding agents and AI assistants.

What are the main limitations of using BaseRT compared to other runtimes?

BaseRT is only compatible with Apple Silicon (M-series) hardware, so it cannot run on Intel-based Macs or non-Apple systems. Additionally, its model support may not cover all open-source models available in other runtimes like llama.cpp or LM Studio.

How is BaseRT priced for developers and teams?

BaseRT is free to use with no paid plan required, making it accessible for individual developers and teams alike. It is open-source and supported by an active community.

Can BaseRT serve models locally for use with coding agents?

Yes, BaseRT allows developers to serve models locally and use them with coding agents, keeping all data on-device. This eliminates the need for API keys or internet connectivity, enhancing privacy and security.

Which open-source models does BaseRT support for inference?

BaseRT supports a range of popular open-source models including Qwen, Llama, Gemma, Mistral, Phi, and Nomic BERT. This coverage enables developers to run diverse language models on Apple Silicon hardware.

Why would a developer choose BaseRT over LM Studio for local model inference?

BaseRT offers faster inference speeds on Apple Silicon, especially during prefill and decode phases, compared to LM Studio. It also keeps all data fully local without requiring internet connectivity, which is ideal for privacy-focused workflows.

Source: basecompute.co

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