Run Llama 3.1 8B on Apple Silicon

Llama 3.1 8B runs comfortably on a base Mac mini M4 with 16 GB of unified memory: the Q4_K_M weights take about 4.9 GB, leaving room for an 8K context. Expect roughly 45–50 tokens/sec on an M4 Pro — fast enough for real-time chat and agent loops.

How much memory does Llama 8B need?

Weight size is parameter count × bits per weight; recommended memory adds 15% runtime headroom plus a KV-cache allowance for the 8,192-token context window.

Which Mac should you pick?

Generation speed on Apple Silicon scales mostly with memory bandwidth, so the chip choice sets both whether the model fits and how fast it runs. Our M4 Pro baseline estimate for Llama 8B at Q4_K_M: ~47 tokens/sec.

Deploy Llama 8B in about 5 minutes

1. 1

   Build a Mac in the constructor — pick an M4 with at least 8 GB of unified memory.

2. 2

   Select the Llama 3.1 8B stack from the catalog — Ollama runtime, weights pre-loaded, OpenAI-compatible API out of the box.

3. 3

   Point your existing OpenAI SDK code at the new base_url and ship.

from openai import OpenAI

client = OpenAI(
    api_key="mcy_live_...",
    base_url="https://dep-xxxx.macyou.cloud/v1",
)
response = client.chat.completions.create(
    model="llama-3.3-8b",
    messages=[{"role": "user", "content": "Hello!"}],
)

License and what it's good at

Llama 3.1 Community License — Free for commercial use below 700M monthly active users; attribution required. Made by Meta.

• Fastest practical Llama for chat, agents, and RAG
• Fits the cheapest Apple Silicon configs (16 GB)
• Well-supported across Ollama, MLX, and llama.cpp

Typical workloads: customer support chatbots; ai agent backends (crewai, langgraph, autogpt); rag pipelines over private documents; rapid prototyping before scaling to 70b.

Frequently asked questions

Related

• Run Llama 3.3 70B on Apple Silicon →
• Run Qwen 2.5 32B on Apple Silicon →
• Deploy Llama 3.1 8B on Apple Silicon: Fast Agents and Chatbots on M4 Pro →
• Buying vs renting a Mac for AI →