Run Llama 3.3 70B on Apple Silicon
Llama 3.3 70B at Q4_K_M takes about 43 GB of memory, so a 64 GB Mac mini M4 Pro is the minimum sensible config and the sweet spot for price. Expect ~12 tokens/sec on an M4 Pro; a Mac Studio M4 Max roughly doubles memory bandwidth (546 GB/s) and generation speed. For Q8 or full precision you move to M4 Max 128 GB or M3 Ultra.
How much memory does Llama 70B 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.
| Quantization | Weights | Recommended RAM | Cheapest fit | Notes |
|---|---|---|---|---|
| Q4_K_M | ~42.8 GB | 56 GB | M4 Pro (Mac mini) | Best quality/size trade-off — the default for most deployments |
| Q8_0 | ~75 GB | 96 GB | M4 Max (Mac Studio) | Near-lossless; noticeably better on code and long reasoning |
| FP16 | ~141.2 GB | 168 GB | M3 Ultra (Mac Studio) | Full precision — research and eval baselines |
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 70B at Q4_K_M: ~12 tokens/sec.
| Chip | Max RAM | Bandwidth | Fits Llama 70B? | From |
|---|---|---|---|---|
| M4 · Mac mini | 32 GB | 120 GB/s | Cluster only | $99/mo |
| M4 Pro · Mac mini | 64 GB | 273 GB/s | Q4 | $199/mo |
| M4 Max · Mac Studio | 128 GB | 546 GB/s | Q4 + Q8 | $286/mo |
| M3 Ultra · Mac Studio | 256 GB | 819 GB/s | Q4 + Q8 + FP16 | $572/mo |
Deploy Llama 70B in about 5 minutes
- 1
Build a Mac in the constructor — pick an M4 Pro with at least 56 GB of unified memory.
- 2
Select the Llama 3.3 70B stack from the catalog — Ollama runtime, weights pre-loaded, OpenAI-compatible API out of the box.
- 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-3.3-70b",
messages=[{"role": "user", "content": "Hello!"}],
)License and what it's good at
Llama 3.3 Community License — Free for commercial use below 700M monthly active users; attribution required. Made by Meta.
- GPT-4-class quality on many tasks, fully private
- The de-facto standard open model for serious workloads
- Q4 fits a Mac mini — the cheapest hardware class that runs a 70B
Typical workloads: production chat and assistant backends; complex reasoning and analysis; private inference for regulated data; fine-tune target via mlx.
Frequently asked questions
What is the cheapest Mac that runs Llama 3.3 70B?
A Mac mini M4 Pro with 64 GB of unified memory runs the Q4_K_M build (~43 GB weights) with room for an 8K context. That is the cheapest single-machine class of hardware that runs a 70B model at usable speed.
How fast is Llama 3.3 70B on Apple Silicon?
Roughly 12 tokens/sec on an M4 Pro at Q4_K_M (our baseline estimate). Generation speed scales with memory bandwidth: M4 Max (546 GB/s) is about 2× the M4 Pro, M3 Ultra (819 GB/s) about 3×.
Do I need a GPU cluster to run a 70B model?
No. Apple Silicon's unified memory means the entire Q4 model fits in one Mac mini's RAM — no multi-GPU sharding, no VRAM juggling. That is exactly why 70B-class self-hosting on Macs is dramatically cheaper than GPU clouds.