DeepSeek R1 Distill has redefined the capabilities of AI by offering high-performance reasoning, coding, and mathematical models optimized for local use on iPhone, iPad, and Mac. With the latest support for these models in Private LLM, users can now experience cutting-edge AI while ensuring complete privacy and offline functionality.

In this blog post, we’ll explore why DeepSeek R1 Distill has captured the AI community’s attention, how you can run these models locally on your Apple devices, and detailed insights into each supported model to help you choose the one that’s right for you.

Screenshot of Private LLM responding to a prompt using DeepSeek R1 Distill model. — DeepSeek R1 Distill demonstrates its reasoning capabilities

What Is DeepSeek R1 Distill?

The DeepSeek R1 Distill series is a collection of AI models distilled from the larger DeepSeek R1 architecture. Using advanced training techniques, these models compress the reasoning power of larger AI models into smaller, more efficient versions. The distillation process fine-tunes these models using data generated by the original DeepSeek R1, ensuring they retain exceptional reasoning and problem-solving abilities.

Loading Tweet...

How to Run DeepSeek R1 Distill Models Locally

Private LLM supports DeepSeek R1 Distill models on iOS v1.9.5+ and macOS v1.9.7+. These models are designed to run entirely offline, leveraging the power of Apple’s hardware.

iOS: Compatible with iPhones and iPads with 8GB RAM or more for smaller models like Llama 8B and Qwen 7B, and 16GB RAM or more for larger models like Qwen 14B.
macOS: Apple Silicon Macs with at least 16GB RAM can run most models, while larger models like Llama 70B require 48GB RAM.

Now let’s dive into each supported model, its base architecture, and its specific use cases.

iOS Models

DeepSeek R1 Distill Llama 8B

Hardware Requirements: iPhones or iPads with 8GB RAM or more (e.g., iPhone 15 Pro, iPad Air).
Base Model: Meta’s Llama 3.1-8B-Base.
Use Case: This model is perfect for mobile users who need reliable AI for reasoning, coding, and problem-solving. Its lightweight architecture ensures smooth operation on mobile devices while delivering robust performance for general-purpose tasks.

Explore DeepSeek R1 Distill Llama 8B on Hugging Face

DeepSeek R1 Distill Qwen 7B

Hardware Requirements: iPhones with 8GB RAM or more (e.g., iPhone 15 Pro).
Base Model: Alibaba’s Qwen-2.5-Math-7B.
Use Case: Ideal for everyday use, this model is optimized for reasoning, symbolic logic, and basic coding tasks. It strikes a balance between efficiency and capability, making it a go-to choice for on-the-go AI.

Explore DeepSeek R1 Distill Qwen 7B on Hugging Face

DeepSeek R1 Distill Qwen 14B

Hardware Requirements: iPad Pro with 16GB RAM (1TB+ storage).
Base Model: Qwen2.5-14B.
Use Case: Designed for more advanced workloads, this model excels in solving complex problems in mathematics and logic. It’s the ideal choice for researchers, analysts, and developers looking to push the limits of mobile AI.

Explore DeepSeek R1 Distill Qwen 14B on Hugging Face

macOS Models

DeepSeek R1 Distill Llama 8B

Hardware Requirements: Apple Silicon Macs with 16GB RAM or more.
Base Model: Meta’s Llama 3.1-8B-Base.
Use Case: This versatile model is tailored for macOS users who need advanced reasoning, coding assistance, and general-purpose problem-solving. It is optimized for low-to-moderate workloads, ensuring smooth performance even on entry-level Apple Silicon Macs.

Explore DeepSeek R1 Distill Llama 8B on Hugging Face

DeepSeek R1 Distill Qwen 7B

Hardware Requirements: Apple Silicon Macs with 16GB RAM or more.
Base Model: Qwen-2.5-Math-7B.
Use Case: A great option for developers and professionals who need enhanced reasoning and coding capabilities on macOS. Its balance between performance and hardware requirements makes it an accessible choice for many users.

Explore DeepSeek R1 Distill Qwen 7B on Hugging Face

DeepSeek R1 Distill Qwen 14B

Hardware Requirements: Apple Silicon Macs with 16GB RAM or more.
Base Model: Qwen2.5-14B.
Use Case: With the ability to handle computationally intensive reasoning and problem-solving tasks, this model is ideal for power users and researchers needing advanced AI tools.

Explore DeepSeek R1 Distill Qwen 14B on Hugging Face

Fuse O1 DeepSeek R1 QwQ SkyT1 32B

Hardware Requirements: Apple Silicon Macs with 32GB RAM or more.
Base Model: A fusion of DeepSeek-R1, QwQ, and SkyT1 architectures.
Use Case: This fusion model is specifically optimized for reasoning-heavy tasks like advanced mathematics, scientific research, and coding. Its innovative architecture delivers unmatched performance in problem-solving.

Explore Fuse O1 DeepSeek R1 QwQ SkyT1 32B on Hugging Face

DeepSeek R1 Distill Llama 70B

Hardware Requirements: Apple Silicon Macs with 48GB RAM or more.
Base Model: Meta’s Llama 3.3-70B-Instruct.
Use Case: The most advanced model in the lineup, Llama 70B is designed for professional-grade workloads. It excels in large-scale AI tasks, such as research, complex reasoning, and coding at an enterprise level.

Explore DeepSeek R1 Distill Llama 70B on Hugging Face

Loading Tweet...

Uncensored Models

DeepSeek R1 Distill Llama 8B Uncensored

Hardware Requirements: iPhones with 8GB RAM or more or Apple Silicon Macs with 16GB RAM.
Base Model: Meta’s Llama 3.1-8B-Base.
Use Case: Fully uncensored for unrestricted AI applications, this model provides robust reasoning capabilities for research, exploratory tasks or NSFW content generation.

Explore DeepSeek R1 Distill Llama 8B Uncensored on Hugging Face

DeepSeek R1 Distill Qwen 32B Uncensored

Hardware Requirements: Apple Silicon Macs with 32GB RAM or more.
Base Model: Alibaba’s Qwen-32B.
Use Case: For users needing uncensored reasoning and computational performance without any NSFW filters.

Explore DeepSeek R1 Distill Qwen 32B Uncensored on Hugging Face

Why Choose Private LLM for DeepSeek R1 Distill Over Ollama or LM Studio?

Private LLM makes it simple to run DeepSeek R1 Distill offline—no internet connection needed. This ensures that your data stays on your device, enhancing privacy and control. By integrating state-of-the-art quantization algorithms like OmniQuant and GPTQ, Private LLM speeds up model inference without sacrificing accuracy. This means you can enjoy top-tier performance even when working entirely offline.

If you've tried DeepSeek R1 Distill on Ollama, LM Studio, or other Llama.cpp/MLX wrappers using naive RTN quantization, you'll notice a difference with Private LLM. OmniQuant and GPTQ quantized models do much better compared to Q4_K_M GGUF quantized models used by llama.cpp based apps like Ollama, and 4-bit RTN quantized MLX models used by MLX based apps like LM Studio (only on macOS, LM Studio currently uses llama.cpp for inference on all other platforms).

Private LLM sets a higher standard for offline model performance, surpassing Ollama and LM Studio in both speed and text output. By running DeepSeek R1 Distill on Private LLM, you're not just keeping your data local—you're also taking advantage of better performance and advanced quantization that helps you get the most out of your hardware.

Don't just take our word for it—compare for yourself.

Download and Install

The DeepSeek R1 Distill models are now available via Private LLM on the App Store. Experience state-of-the-art local AI reasoning models by downloading Private LLM today.

Explore these incredible models with full privacy and subscription free. Download Now.