DeekSeek System Requirements Guide to Run on Macbook and Mac Studio

Updated: March 08 2025 11:04

Running machine learning models like DeepSeek on macOS has become increasingly practical with advancements in Apple Silicon's unified memory and processing power. DeepSeek is a powerful tool for various AI applications, but it requires significant resources depending on the model size. DeepSeek R1 distinguishes itself through its innovative training methodology, primarily leveraging large-scale reinforcement learning (RL) with minimal supervised fine-tuning. This approach enabled the model to develop robust reasoning abilities organically. This guide covers everything you need to know about Mac hardware specifications for DeepSeek, including full and quantized models, memory needs, and recommended devices.


The initial iteration, DeepSeek-R1-Zero, was trained exclusively using Group Relative Policy Optimization (GRPO), a variant of policy gradient methods that eliminates the need for a separate "critic" model by normalizing rewards within a group of generated outputs, reducing computational cost. This method allowed the model to learn from vast amounts of inference data without direct human supervision, resulting in notable reasoning capabilities. However, challenges such as repetition and readability issues were observed in its outputs.

To address these challenges, the development of DeepSeek R1 incorporated initial supervised data before RL training. This integration aimed to improve output readability and coherence while maintaining the robust reasoning skills developed through reinforcement learning. The combination of supervised fine-tuning and reinforcement learning resulted in a model capable of delivering more accurate and user-friendly responses.

Running large language models (LLMs) like DeepSeek R1 locally on devices such as the new Mac Studio with M3 Ultra offers several significant advantages:

• Enhanced Privacy and Data Security: Keeping data on local devices ensures that sensitive information remains secure, reducing the risk of exposure associated with transmitting data to external servers.
• Reduced Latency and Improved Performance: Local deployment eliminates the delays inherent in cloud-based processing, leading to faster response times and a more seamless user experience.
• Cost Efficiency: Operating LLMs locally can lower expenses by removing the need for ongoing cloud service subscriptions and data transfer fees.
• Customization and Control: Users have greater flexibility to tailor models to specific requirements, optimizing performance and compliance with particular regulations.
• Offline Accessibility: Local models provide the capability to function without internet connectivity, ensuring continuous operation in environments with limited or unreliable network access.

DeepSeek models vary widely in size, from smaller variants with a few billion parameters to massive models exceeding 600 billion parameters. The memory requirements for these models are substantial, especially for full-precision (FP16) models. For instance, the DeepSeek-LLM 7B model, with 7 billion parameters, requires approximately 16 GB of unified memory, making it suitable for devices like the MacBook Air with M3 chip and 24 GB RAM. On the other end of the spectrum, the DeepSeek V3 671B model, boasting 671 billion parameters, demands around 1,543 GB of unified memory, necessitating a distributed setup across multiple high-end Mac Studio machines equipped with M2 Ultra chips and 192 GB RAM each.

Quantization offers a solution to reduce these hefty memory requirements. By employing lower precision, such as 4-bit quantization, the memory footprint of DeepSeek models can be significantly decreased. For example, the DeepSeek-LLM 7B model's memory requirement drops to approximately 4 GB when quantized, making it feasible to run on a MacBook Air with an M2 chip and 8 GB RAM. Similarly, the DeepSeek V3 671B model's memory needs are reduced to around 386 GB with 4-bit quantization, allowing it to run on a setup of three Mac Studio machines with M2 Ultra chips and 192 GB RAM each.

DeepSeek-R1 achieves performance comparable to OpenAI-o1 across math, code, and reasoning tasks. Here is the performance comparison:





Selecting the appropriate Apple Silicon device depends on the size of your DeepSeek models and their memory requirements. Here's a breakdown of recommendations:

• M2/M3/M4 MacBook Air (16GB–24GB): Ideal for small quantized models with fewer than 7 billion parameters such as the deepseek-r1:1.5b or deepseek-r1:7b models.
• M2/M3/M4 MacBook Pro or Mac Mini M4 (32GB-64GB): Suitable for mid-range models and some smaller full-precision models such as the deepseek-r1:8b or deepseek-r1:14b models.
• M2 Max/Ultra or M4 Max/M3 Ultra Mac Studio (192GB+): Best suited for large full-precision models such as the deepseek-r1:32b or deepseek-r1:70b models.
• Mac Pro and distributed setup (Future Consideration): Potentially a key option for extremely large models requiring higher compute & memory.

It's important to note that these recommendations are based on the bare minimum requirements for running the models, assuming no other applications are running on the Mac. For optimal performance and larger context lengths, it's advisable to have more than the minimum required memory. If your available memory barely fits the model, you may need to adjust parameters such as batch size to ensure smooth operation.

Here is the performance of running various DeepSeek models on Mac Mini M4 64G ram with 14/20 Cores/GPUs:

deepseek-r1:32b

total duration:       1m22.986158458s
load duration:        29.640208ms
prompt eval count:    251 token(s)
prompt eval duration: 3.577s
prompt eval rate:     70.17 tokens/s
eval count:           827 token(s)
eval duration:        1m19.377s
eval rate:            10.42 tokens/s

deepseek-r1:14b

total duration:       2m31.563164958s
load duration:        35.015416ms
prompt eval count:    234 token(s)
prompt eval duration: 1.441s
prompt eval rate:     162.39 tokens/s
eval count:           742 token(s)
eval duration:        2m30.084s
eval rate:            4.94 tokens/s

Apple's recent release of the updated Mac Studio, featuring the M4 Max and M3 Ultra chips, marks a significant advancement in desktop computing. These new configurations offer substantial improvements in processing power, memory capacity, and connectivity, catering to professionals with demanding computational needs. The Mac Studio now comes equipped with two powerful chip options:

• M4 Max: This chip features a 16-core CPU and up to a 40-core GPU, starting with 36GB of unified memory, expandable up to 128GB.
• M3 Ultra: Boasting a 32-core CPU and up to an 80-core GPU, the M3 Ultra supports up to 512GB of unified memory, providing exceptional performance for intensive tasks.

Both configurations include Thunderbolt 5 ports, enhancing data transfer speeds and peripheral connectivity.

Here is a leaked benchmark showing the M3 Ultra performance on the Mac Studio. It shows a single-core score of 3221, which is approximately 16% faster than M2 Ultra. The M3 Ultra multi core score is 27749, or approximately 30% faster than the M2 Ultra. In Metal GPU benchmarking, the M2 Ultra scored 221824, compared to the M3 Ultra's 259668, which is faster by 17%:

Breaking: M3 Ultra benchmarks have leaked, confirming that it’s now the world’s FASTEST production CPU.

Scores are a bit low because of old M3 core tech and diminishing returns with more cores, thanks to how the multi-core update works with Geekbench 6. https://t.co/WWoSiQ7zWD

— Vadim Yuryev (@VadimYuryev) March 7, 2025

The M3 Ultra chip is designed with a comprehensive suite of features tailored for AI workloads:

• 32-Core CPU: This high-performance CPU is optimized for heavily threaded tasks, ensuring rapid data processing and analysis.
• Up to 80-Core GPU: The powerful GPU delivers exceptional graphics rendering capabilities, supporting complex computations essential for AI model training and inference.
• Enhanced Neural Engine: With double the Neural Engine cores compared to previous models, the M3 Ultra accelerates machine learning computations, enabling faster execution of AI algorithms.
• UltraFusion Architecture: This innovative design links two M3 Max dies over 10,000 high-speed connections, allowing the chip to operate as a single cohesive unit. This architecture ensures high performance while maintaining energy efficiency.
• Over 800GB/s Memory Bandwidth: The substantial memory bandwidth facilitates swift data access and transfer, crucial for handling large datasets and complex models inherent in AI tasks.

The M3 Ultra's robust architecture enables AI professionals to run large language models (LLMs) with over 600 billion parameters directly on the Mac Studio. This capability allows for on-device processing, reducing reliance on cloud-based solutions and enhancing data privacy and security. The integration of high-performance CPU and GPU cores, coupled with an advanced Neural Engine, ensures that the Mac Studio can handle the computational demands of extensive AI models efficiently.

The 512GB of unified memory in the M3 Ultra model addresses the growing demands for high-bandwidth memory in professional applications. Achieving such bandwidth with traditional DDR5 setups poses challenges, often requiring multiple CPU complexes to surpass 500GB/s. For instance, AMD's EPYC 9355P processor, priced at $2,998, offers 106GB/s per CCD, necessitating five CCDs to exceed 500GB/s, which increases complexity and cost.

In contrast, Apple's unified memory architecture in the Mac Studio provides a more streamlined solution, albeit at a premium price point. The 512GB configuration of the Mac Studio is priced at $9,500 in the United States and €11,000 in Europe, with educational discounts reducing the U.S. price to approximately $8,600.

The enhanced memory and processing capabilities of the new Mac Studio make it particularly appealing for AI and machine learning tasks. Users have expressed interest in deploying models like DeepSeek R1 and handling large language model (LLM) token generation locally. The 512GB unified memory allows for efficient hosting of such models, offering a viable alternative to setups like multiple RTX 3090 GPUs. However, it's important to note that while the Mac Studio provides substantial memory bandwidth, the cost of server-grade RAM and the overall system investment remain significant considerations for potential users.

For comparison, here is the performance of running various DeepSeek R1 models on 4090 GPU:

deepseek-r1:1.5b

total duration:       3.234323324s
load duration:        9.759262ms
prompt eval count:    24 token(s)
prompt eval duration: 11ms
prompt eval rate:     2181.82 tokens/s
eval count:           1050 token(s)
eval duration:        3.212s
eval rate:            326.90 tokens/s
memory usage:	1973MiB / 24564MiB

deepseek-r1:7b

total duration:       7.626323748s
load duration:        9.493629ms
prompt eval count:    24 token(s)
prompt eval duration: 17ms
prompt eval rate:     1411.76 tokens/s
eval count:           1113 token(s)
eval duration:        7.598s
eval rate:            146.49 tokens/s
memory usage:	5625MiB / 24564MiB

deepseek-r1:8b

total duration:       15.457397942s
load duration:        10.024962ms
prompt eval count:    24 token(s)
prompt eval duration: 92ms
prompt eval rate:     260.87 tokens/s
eval count:           2131 token(s)
eval duration:        15.354s
eval rate:            138.79 tokens/s
memory usage:	6507MiB / 24564MiB

deepseek-r1:14b

total duration:       14.432098376s
load duration:        9.027491ms
prompt eval count:    24 token(s)
prompt eval duration: 104ms
prompt eval rate:     230.77 tokens/s
eval count:           1135 token(s)
eval duration:        14.317s
eval rate:            79.28 tokens/s
memory usage:	10941MiB / 24564MiB

deepseek-r1:32b

total duration:       55.242498456s
load duration:        9.446347ms
prompt eval count:    25 token(s)
prompt eval duration: 124ms
prompt eval rate:     201.61 tokens/s
eval count:           2173 token(s)
eval duration:        55.108s
eval rate:            39.43 tokens/s
memory usage:	21853MiB / 24564MiB

The Mac Studio's pricing varies based on configuration: (both models are available for preorder with shipments commencing on March 12)

• M4 Max Model: Starts at $1,999 with 36GB of RAM and 512GB of storage.
• M3 Ultra Model: Begins at $3,999 with 96GB of RAM and 1TB of storage.
• M3 Ultra Model: 32-core CPU, 80-core GPU is $9,499 with the highest 512GB of RAM and 1TB of storage.

Running DeepSeek models on macOS is feasible with proper hardware planning. Smaller models can run efficiently on MacBook Air or MacBook Pro devices, but larger models require powerful configurations like the updated Mac Studio with M4 Max and M3 Ultra chips offers professionals enhanced performance and memory capabilities, making it a strong contender for high-demand computing tasks. Prospective buyers should weigh the benefits against the associated costs to determine the best configuration for their specific needs.

DeepSeek R1: Research Paper
DeekSeek R1: Hugging Face
Apple M3 Ultra: Apple reveals M3 Ultra, taking Apple silicon to a new extreme
Apple Mac Studio: Apple unveils new Mac Studio, the most powerful Mac ever, featuring M4 Max and new M3 Ultra