Skip to content

text model · Llama · macOS

Can I run Llama 3.3 70B on Apple M4 Max (64GB)?

Compatibility verdict VRAM threshold engine
Yes, but tight usable speed ~10 tok/s est.

Yes. Llama 3.3 70B runs on Apple M4 Max (64GB) at Q4_K_M (~45.3 GB of ~48 GB usable).

Needs ~45.3 GB Device usable ~48 GB

Fits at Q4_K_M (~45.3 GB of ~48 GB usable) but with little headroom. Close other apps, or drop to a 2k context to free about a gigabyte.

That figure is at a 4k context and moves about ±15% as context length changes. Apple M4 Max (64GB) leaves ~2.7 GB of headroom.

Q4_K_M needed
~45.3 GB
Usable on device
~48 GB
Device memory
64 GB
Best quant
Q4_K_M
Share on X Share on Reddit

Which quant fits

Quant ladder vs ~48 GB usable
Q2_K
~32.1 GB
Q3_K_M
~37 GB
Q4_K_M
~45.3 GB
Q5_K_M
~52.7 GB
Q6_K
~60.2 GB
Q8_0
~77.8 GB
FP16
~142.8 GB
The line marks Apple M4 Max (64GB)'s ~48 GB budget; rungs past it are too large.

Running cost · estimate

Power & economics est.
Power draw
~145 W
Electricity / 1M tokens
~$0.6

At ~$0.15/kWh and the estimated ~10 tok/s, a million generated tokens costs about $0.6 in electricity. TDP is the peak draw, so this is an upper bound. Assumptions.

Run it

Install commands macOS

Pick your tool. All 3 load the same Q4_K_M weights.

Ollama
$ ollama run llama3.3:70b
llama.cpp
$ llama-cli -hf bartowski/Llama-3.3-70B-Instruct-GGUF:Q4_K_M
LM Studio
$ lms get bartowski/Llama-3.3-70B-Instruct-GGUF
Model Llama
Parameters
70B
Q4_K_M size
42.52 GB
Q8_0 size
74.98 GB
Context
128k
Ollama tag
llama3.3:70b
Full Llama 3.3 70B requirements →
Device macOS
Memory
64 GB unified
Usable for weights
~48 GB
Power draw
~145 W
Best runtime
MLX direct / Ollama (MLX backend)
Best models for Apple M4 Max (64GB) →

You could also run

Run Llama 3.3 70B on other hardware

FAQ

Can Apple M4 Max (64GB) run Llama 3.3 70B?

Yes. Llama 3.3 70B runs on Apple M4 Max (64GB) at Q4_K_M (~45.3 GB of ~48 GB usable).

How much memory does Llama 3.3 70B need?

It is a tight fit on Apple M4 Max (64GB). At Q4_K_M the weights are ~42.52 GB; with KV cache and runtime overhead, budget ~45.3 GB at a 4k context.

What is the best tool to run Llama 3.3 70B on macOS?

LM Studio for a simple setup; mlx-lm for the most speed. vLLM is NOT a Mac tool, it is a CUDA/Linux serving engine. Unified memory is not a fixed VRAM slice; ~70% is usable for weights.

Embed this

Llama 3.3 70B on Apple M4 Max (64GB) compatibility badge A live badge for your model card or README, updated as the data is.
Markdown
$ [![Llama 3.3 70B on Apple M4 Max (64GB)](https://localmodel.run/badge/llama-3.3-70b/apple-m4-max-64gb.svg)](https://localmodel.run/can-i-run/llama-3.3-70b/apple-m4-max-64gb)

Sources

Weights are measured from GGUF files; KV cache and overhead are computed, so totals can vary ~15% with context and runtime. Any tok/s is a bandwidth estimate. See methodology.