Qwen3 8B (Q8_K_XL) — 17 GBon Apple M4 Max 128GB
Overview
Qwen3 8B is a 8B parameter dense language model by Qwen, with code, multilingual, thinking, tool-calls capabilities. It supports a context window of up to 40,960 tokens.
Qwen3 8B is an 8-billion-parameter dense transformer from Alibaba's Qwen team, featuring built-in thinking capabilities alongside code generation, tool calling, and multilingual support. It advances beyond Qwen2.5 with improved reasoning, supporting chain-of-thought inference in a compact form factor. The model covers 14 languages including English, Chinese, and Arabic. With a 40K context window and flash attention, it fits on a single consumer GPU and quantizes efficiently for cost-effective self-hosted reasoning workloads.
At Q8_K_XL quantization (high quality tier), the model weighs 10.08 GB. This fits within the 128 GB of VRAM on Apple M4 Max 128GB, enabling full GPU inference.
Hardware Requirements
| Model size | 10.08 GB |
| VRAM available | 128 GB |
| VRAM used | 17 GB |
| System RAM | |
| Min RAM required | 0 GB |
| GPU layers | 36 / 36 |
| Context size | 40,960 |
| Backend | metal |
| Flash attention | Yes |
| Reading from disk | Yes |
Deploy
Install llama.cpp
brew install llama.cppDownload Model
curl -L -o qwen3-8b.gguf "https://huggingface.co/unsloth/Qwen3-8B-GGUF/resolve/main/Qwen3-8B-UD-Q8_K_XL.gguf"Start Server
llama-server \
-m qwen3-8b.gguf \
--n-gpu-layers 36 \
--ctx-size 40960 \
--flash-attnVerify
curl http://localhost:8080/healthFrequently Asked Questions
How much VRAM does Qwen3 8B (Q8_K_XL) need?
The Q8_K_XL quantization of Qwen3 8B requires 10.08 GB. All 36 layers fit in the 128 GB of VRAM available on Apple M4 Max 128GB, enabling full GPU acceleration.
Can I run Qwen3 8B on Apple M4 Max 128GB?
Yes. Apple M4 Max 128GB provides 128 GB of VRAM, which is enough to run Qwen3 8B (Q8_K_XL) with all layers on the GPU for optimal performance.
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. Q8_K_XL compresses Qwen3 8B from its original size down to 10.08 GB.
What quantization should I choose for Qwen3 8B?
Q8_K_XL is a high-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 Qwen3 8B on Apple M4 Max 128GB, flash attention is enabled to maximise context length and throughput within the available 128 GB of VRAM.
How do I run Qwen3 8B (Q8_K_XL) with Ollama?
Run ollama run qwen3:8b-q8_k_xl to start Qwen3 8B (Q8_K_XL). Ollama handles downloading the model weights automatically on first run.