Qwen3.5 2B (Q3_K_XL) — 14.4 GBon Apple M3 Max 64GB
Overview
Qwen3.5 2B is a 2.27B parameter dense language model by Qwen, with code, multilingual, thinking, tool-calls, vision capabilities. It supports a context window of up to 262,144 tokens.
Qwen3.5 2B is a lightweight model from Alibaba's Qwen 3.5 family built on the Gated Delta Networks hybrid architecture with 2.27 billion parameters, balancing capability and efficiency for edge deployment. It is natively multimodal, processing text, images, and video, with built-in thinking capabilities for chain-of-thought reasoning. The model supports a 262K context window and covers over 201 languages, handling code generation and multilingual tasks with ease. Released under the Apache 2.0 license, it runs in roughly 2 GB of VRAM at Q4, making it practical for self-hosted deployment on modest hardware.
At Q3_K_XL quantization (low quality tier), the model weighs 1.08 GB. This fits within the 64 GB of VRAM on Apple M3 Max 64GB, enabling full GPU inference.
Hardware Requirements
| Model size | 1.08 GB |
| VRAM available | 64 GB |
| VRAM used | 14.4 GB |
| System RAM | |
| Min RAM required | 0 GB |
| GPU layers | 24 / 24 |
| Context size | 262,144 |
| Backend | metal |
| Flash attention | Yes |
| Reading from disk | Yes |
Deploy
Install llama.cpp
brew install llama.cppDownload Model
curl -L -o qwen3-5-2b.gguf "https://huggingface.co/unsloth/Qwen3.5-2B-GGUF/resolve/main/Qwen3.5-2B-UD-Q3_K_XL.gguf"Start Server
llama-server \
-m qwen3-5-2b.gguf \
--n-gpu-layers 24 \
--ctx-size 262144 \
--flash-attnVerify
curl http://localhost:8080/healthFrequently Asked Questions
How much VRAM does Qwen3.5 2B (Q3_K_XL) need?
The Q3_K_XL quantization of Qwen3.5 2B requires 1.08 GB. All 24 layers fit in the 64 GB of VRAM available on Apple M3 Max 64GB, enabling full GPU acceleration.
Can I run Qwen3.5 2B on Apple M3 Max 64GB?
Yes. Apple M3 Max 64GB provides 64 GB of VRAM, which is enough to run Qwen3.5 2B (Q3_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. Q3_K_XL compresses Qwen3.5 2B from its original size down to 1.08 GB.
What quantization should I choose for Qwen3.5 2B?
Q3_K_XL 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 Qwen3.5 2B on Apple M3 Max 64GB, flash attention is enabled to maximise context length and throughput within the available 64 GB of VRAM.
How do I run Qwen3.5 2B (Q3_K_XL) with Ollama?
Run ollama run qwen3.5:2b-q3_k_xl to start Qwen3.5 2B (Q3_K_XL). Ollama handles downloading the model weights automatically on first run.