Private LLM now supports the Qwen 2.5 and Qwen 2.5 Coder models, offering cutting-edge performance for AI tasks locally on macOS and iOS. This update brings models ranging from 0.5B to 32B parameters, optimized for local use and enhanced by efficient GPTQ quantization. Whether you are a developer, researcher, or an AI enthusiast, this release makes advanced language model capabilities more accessible while preserving your privacy.

How Qwen 2.5 Excels in Performance Benchmarks:

Benchmarks provide a quantifiable way to assess the capabilities of AI models across coding, reasoning, and text generation tasks. Below are detailed results for Qwen 2.5 models, showing their strengths compared to other leading models.

Qwen 2.5 Coder Coding Performance:

The HumanEval benchmark tests a model's ability to solve coding problems in Python. The MBPP benchmark focuses on small programming tasks. These benchmarks demonstrate Qwen 2.5 Coder's exceptional coding capabilities.

Benchmark	Qwen 2.5 Coder 32B	GPT-4o	Claude 3.5 Sonnet	DeepSeek Coder V2	CodeStral 22B
HumanEval	92.7	92.1	92.1	88.4	78.1
MBPP	90.2	86.8	91.0	89.2	73.3
LiveCodeBench	31.4	34.6	31.6	27.9	22.6

Qwen 2.5 Coder performs comparably to GPT-4o and Claude 3.5 Sonnet in coding accuracy but offers the advantage of being accessible locally through Private LLM.

Private LLM running Qwen 2.5 Coder 32B on macOS, explaining the iterative and recursive approaches to implementing a binary search algorithm in Python.

Qwen 2.5 32B Reasoning and Knowledge:

The MMLU-redux and MATH benchmarks evaluate reasoning, knowledge, and mathematical problem-solving capabilities.

Benchmark	Qwen 2.5 32B	GPT4-o Mini	Gemma2-27B	Qwen 2.5 14B
MMLU-redux	83.9	81.5	75.7	80.0
MATH	83.1	70.2	54.4	80.0
GSM8K	95.9	93.2	90.4	94.8

Takeaway: Qwen 2.5 32B excels in tasks requiring general knowledge and problem-solving, surpassing many competitors.

Private LLM with Qwen 2.5 7B on iPhone, translating a paragraph into French and providing a summary of the translation.

Device Compatibility and RAM Requirements

Running advanced models like Qwen 2.5 locally requires sufficient RAM to handle the computational workload. Below are the detailed requirements for macOS and iOS devices.

macOS RAM Requirements

Model Name	Minimum RAM	Context Length
Qwen 2.5 0.5B Unquantized	Any Apple Silicon Mac	32k
Qwen 2.5 7B GPTQ	16GB or more	8k (32k on 16GB or higher)
Qwen 2.5 Coder 14B GPTQ	16GB or more	8k (32k on 24GB or higher)
Qwen 2.5 Coder 32B GPTQ	24GB or more	8k (32k on 48GB or higher)

iOS RAM Requirements

Model Name	Minimum RAM	Device Type
Qwen 2.5 1.5B GPTQ	4GB	Most iOS devices
Qwen 2.5 7B GPTQ	8GB	iPhone Pro models
Qwen 2.5 Coder 14B GPTQ	16GB	High-end (1TB+) Apple Silicon iPads

Private LLM with Qwen 2.5 Coder 7B on iPad, analyzing time and space complexity of a Python function and suggesting improvements.

Private LLM vs. Other Local Apps: What Sets It Apart

Private LLM outperforms other local LLM inference apps like Ollama, LM Studio, or any other Llama.cpp/MLX wrapper in two critical areas:

1. Faster Inference Performance:

Models optimized with GPTQ quantization in Private LLM run faster, providing quicker responses even for complex tasks, compared to RTN-quantized models used by other apps.

2. Improved Model Perplexity:

GPTQ quantization maintains high model fidelity, enabling Qwen 2.5 models to produce more coherent and contextually accurate responses than alternatives.

With the integration of Qwen 2.5 and Qwen 2.5 Coder models, Private LLM continues to lead in delivering advanced AI capabilities locally. These models offer exceptional performance across coding, reasoning, and multilingual tasks, all while running entirely locally.

Ready to experience the difference? Download Private LLM and start exploring Qwen 2.5 today.