How to Run Llama 3 70B on a Mac with 128 GB RAM

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TL;DR: You need an M4 Max or M3 Ultra Mac with at least 128 GB of unified memory to run Llama 3 70B comfortably. The best setup is MLX through LM Studio — expect ~11–12 tok/s at Q4 quantization, which is conversational speed. If you have 96 GB instead of 128 GB, you can still run 70B at tight Q4 quantization with limited context, or comfortably run it at Q3.

Hardware Requirement: Which Macs Have 128 GB

Not every Mac supports 128 GB of unified memory. Here are the chips that do:

M4 Max (Mac Studio, MacBook Pro 16") — 128 GB max, 546 GB/s bandwidth
M3 Max (MacBook Pro 14"/16", Mac Studio) — 128 GB max, 400 GB/s bandwidth
M3 Ultra (Mac Studio) — up to 512 GB, 819 GB/s bandwidth
M2 Ultra (Mac Pro, Mac Studio) — up to 192 GB, 800 GB/s bandwidth
M1 Ultra (Mac Studio) — 128 GB max, 800 GB/s bandwidth

The Mac Mini tops out at 64 GB (M4 Pro). The MacBook Air tops out at 32 GB. Neither can run 70B comfortably.

Our recommendation: The Mac Studio M4 Max with 128 GB (~~$3,950 as of February 2026) is the best price-to-performance option. The MacBook Pro M4 Max 128 GB (~~$4,800+) gives you portability at a premium. If you already own an older M1/M2 Ultra Mac Studio, it'll work — just with slightly different performance characteristics based on the chip's bandwidth.

For a full breakdown of which Mac model to buy and how they compare across memory tiers, see the complete Mac comparison guide. For how the M4 Max compares against a high-end NVIDIA GPU, see M4 Max vs RTX 4090.

Which Quantization Fits in 128 GB

Llama 3 70B comes in different quantization levels, each trading quality for size:

Notes

Noticeable quality loss. Not recommended — you have the RAM for better.

Moderate quality loss. Usable for testing, not production.

The sweet spot. Minimal quality loss compared to full precision. ~88 GB free for OS, apps, and KV cache.

Higher quality. Fits with ~73 GB headroom. Worth it if you value response quality.

Near full-precision quality. Fits with ~48 GB headroom. Slower at ~6.2 tok/s but the highest quality you can run on 128 GB.

Full precision. Does not fit in 128 GB. Our recommendation: Start with Q4_K_M for the best balance of speed and quality. Step up to Q8 when you need maximum quality and don't mind slower generation. Skip Q2 and Q3 — if you have 128 GB, there's no reason to use them.

Not sure what quantization level matches your use case? The VRAM guide breaks down model sizes across all quantization levels.

Software: MLX vs llama.cpp on Apple Silicon

Two frameworks dominate local LLM inference on Mac:

MLX (via LM Studio or mlx-lm):

Built by Apple specifically for Apple Silicon
20–30% faster than llama.cpp for token generation on most models
Native Metal GPU acceleration
Models from the mlx-community on Hugging Face are pre-converted
Best for: Interactive chat, API serving, anything where generation speed matters

llama.cpp (via Ollama or CLI):

Cross-platform, GGUF model format
Slightly slower than MLX for generation, but better at long-context inference
Flash attention support (--flash-attn) makes it 2x faster for 30K+ token contexts
Best for: Long-context RAG, document processing, compatibility with existing workflows

Our recommendation: Use LM Studio with the MLX backend for daily use. It gives you the fastest generation speed with a clean GUI. Use Ollama (which wraps llama.cpp) if you need an API-compatible endpoint or you're working with very long context windows.

For benchmark numbers showing how MLX performance scales across every M-series chip, see the Apple Silicon LLM benchmarks.

Step-by-Step Setup Walkthrough

Option A: LM Studio (Recommended — Easiest)

Download LM Studio from lmstudio.ai
Open LM Studio and search for "Llama 3 70B"
Select a Q4_K_M or MLX 4-bit model from mlx-community
Click Download (this will take 20–40 minutes on a fast connection — the file is ~40 GB)
Once downloaded, select the model and click Load
Start chatting. LM Studio auto-detects your Apple Silicon and uses the optimal backend.

Option B: Ollama (CLI, API-compatible)

# Install Ollama
curl -fsSL https://ollama.com/install.sh | sh

# Download and run Llama 3.1 70B
ollama run llama3.1:70b

# Ollama auto-selects Q4_K_M by default
# The model downloads to ~/.ollama/models/

Option C: mlx-lm (Python, most control)

# Install mlx-lm
pip install mlx-lm

# Run inference
mlx_lm.generate \
  --model mlx-community/Meta-Llama-3.1-70B-Instruct-4bit \
  --prompt "Explain quantum computing in simple terms" \
  --max-tokens 500

Expected Inference Speed and Temperature Performance

Generation speed on M4 Max 128 GB (546 GB/s):

Llama 3.3 70B Q4: ~11.8 tok/s
Llama 3.1 70B Q4: ~11–12 tok/s
Llama 3.1 70B Q8: ~6.2 tok/s

Generation speed on M3 Max 128 GB (400 GB/s):

Llama 3 70B Q4: ~8–9 tok/s

Prompt processing (prefill):

Short prompts (under 1,000 tokens): ~150–200 tok/s
Long prompts (4,000+ tokens): ~80–120 tok/s

Thermals:

MacBook Pro M4 Max under sustained inference: CPU/GPU temps 95–103°C, fans audible
Mac Studio M4 Max under sustained inference: GPU temps 75–85°C, nearly silent
Neither will thermal throttle during normal inference sessions, but the Mac Studio maintains more consistent boost clocks over extended periods

Tips for Sustained Performance Without Throttling

Use a Mac Studio if possible. Desktop thermals beat laptop thermals for sustained workloads. The Mac Studio's fan rarely exceeds a gentle hum even under full 70B inference.

Close unnecessary apps. Each GB of memory used by other apps is a GB not available for model context. Close Chrome (a notorious memory hog) or at least limit tabs.

Use Q4 over Q8 for sustained sessions. Q4 generates at ~12 tok/s vs. ~6 tok/s for Q8. For multi-hour coding or research sessions, the speed difference compounds significantly.

Set context length deliberately. Don't default to maximum context. A 4K context window uses far less memory than 32K, leaving more headroom for the model weights and reducing prompt processing time.

On MacBook Pro: Use the laptop on a flat, hard surface — not on a bed or couch. Elevate the back slightly for better airflow. Consider a laptop cooling pad for sessions longer than 30 minutes.

What to Do with 96 GB If You Can't Afford 128 GB

If 128 GB is out of budget, a 96 GB Mac (M4 Max or M3 Max) can still run 70B:

Llama 3 70B Q4_K_M (~40 GB): Fits with ~56 GB free. Comfortable with moderate context lengths (up to 8K tokens).
Llama 3 70B Q6_K (~55 GB): Fits with ~41 GB free. Tight but functional.
Llama 3 70B Q8 (~80 GB): Fits with only ~16 GB free. Very tight — limit context to 2K tokens and close all other apps.

96 GB is the minimum for comfortable 70B Q4 usage. You give up Q8 quality and long context windows compared to 128 GB, but for standard chat-length interactions, 96 GB works.

The 64 GB option: A 64 GB Mac (M4 Pro or M4 Max) can technically load 70B Q4 (~40 GB model), but with only ~24 GB left for OS + context, you'll be limited to short conversations and may experience swapping. It works in a pinch — just don't expect it to be comfortable.

Related:

Best Mac for Running Local LLMs in 2026 — Which Mac to buy
M4 Max vs RTX 4090 for Local LLMs — Mac vs PC for 70B models
M4 Pro vs M4 Max for Local AI: Is the Max Chip Worth It?
Apple Silicon LLM Benchmarks: Every M-Series Chip Tested
The $3,000 Dual-GPU LLM Rig — PC alternative for running 70B