Llama 3.3 70B Instruct (Q4_0_4_8) — 15.4 GBon Apple M2 Pro 16GB
Overview
Llama 3.3 70B Instruct is a 70B parameter dense language model by Meta, with code, multilingual, tool-calls capabilities. It supports a context window of up to 131,072 tokens.
Llama 3.3 70B Instruct is a 70-billion-parameter dense transformer model from Meta, optimized for instruction following, code generation, and multilingual conversation. It delivers performance competitive with larger models in the Llama family while remaining practical for single-node GPU deployments. The model supports tool calling and eight languages including English, French, Spanish, and German. With a 128K context window and grouped-query attention, it quantizes efficiently down to Q4 levels for self-hosted inference on consumer hardware.
At Q4_0_4_8 quantization (low quality tier), the model weighs 37.22 GB. This exceeds the 16 GB of VRAM on Apple M2 Pro 16GB. Inference is still possible via CPU offload or memory-mapped loading from disk, but expect significantly reduced performance.
Hardware Requirements
| Model size | 37.22 GB |
| VRAM available | 16 GB |
| VRAM used | 15.4 GB |
| System RAM | |
| Min RAM required | 23.3 GB |
| GPU layers | 30 / 80 |
| Context size | 648 |
| Backend | metal |
| Flash attention | Yes |
| Reading from disk | Yes |
Performance Notes
Deploy
Install llama.cpp
brew install llama.cppDownload Model
curl -L -o llama-3-3-70b-instruct.gguf "https://huggingface.co/bartowski/Llama-3.3-70B-Instruct-GGUF/resolve/main/Llama-3.3-70B-Instruct-Q4_0_4_8.gguf"Start Server
llama-server \
-m llama-3-3-70b-instruct.gguf \
--n-gpu-layers 30 \
--ctx-size 648 \
--flash-attnVerify
curl http://localhost:8080/healthFrequently Asked Questions
How much VRAM does Llama 3.3 70B Instruct (Q4_0_4_8) need?
The Q4_0_4_8 quantization of Llama 3.3 70B Instruct requires 37.22 GB. 30 of 80 layers fit in the 16 GB of VRAM on Apple M2 Pro 16GB; remaining layers are offloaded to CPU.
Can I run Llama 3.3 70B Instruct on Apple M2 Pro 16GB?
Yes, with reduced performance. Apple M2 Pro 16GB can run Llama 3.3 70B Instruct (Q4_0_4_8), but only 30 of 80 layers fit in VRAM. The rest are offloaded to CPU.
What is quantization?
Quantization reduces a model's numerical precision from its original floating-point format to a more compact representation. This shrinks the file size and VRAM footprint, making it possible to run large models on consumer hardware. The trade-off is a small reduction in output quality. Q4_0_4_8 compresses Llama 3.3 70B Instruct from its original size down to 37.22 GB.
What quantization should I choose for Llama 3.3 70B Instruct?
Q4_0_4_8 is a low-quality quantization. Higher-quality quants (Q8, Q6) preserve more model accuracy but need more VRAM. Lower quants (Q4, Q3, Q2) reduce VRAM usage at the cost of some quality. Choose based on your available hardware and quality requirements.
What is flash attention and why is it enabled?
Flash attention is a memory-efficient algorithm that speeds up the attention mechanism in transformer models. It reduces VRAM usage during inference by avoiding the materialisation of the full attention matrix. For Llama 3.3 70B Instruct on Apple M2 Pro 16GB, flash attention is enabled to maximise context length and throughput within the available 16 GB of VRAM.
Why are some layers offloaded to CPU?
Apple M2 Pro 16GB has 16 GB of VRAM, but Llama 3.3 70B Instruct (Q4_0_4_8) requires approximately 37.22 GB. Only 30 of 80 layers fit in VRAM; the remaining layers run on CPU, which is slower but still functional.
How do I run Llama 3.3 70B Instruct (Q4_0_4_8) with Ollama?
Run ollama run llama3.3:70b-instruct-q4_0_4_8 to start Llama 3.3 70B Instruct (Q4_0_4_8). Ollama handles downloading the model weights automatically on first run.