Mac Mini M4 vs M3 vs AMD vs NVIDIA: LLM Performance Showdown

Introduction

The world of computing is constantly evolving, with new processors and graphics cards hitting the market at a rapid pace. For professionals and enthusiasts working with large language models (LLMs), the performance of these components is crucial. In this article, we'll dive deep into a comparison between the new Mac Mini M4 processor, the M3, AMD GPU, and NVIDIA GPU, focusing on their capabilities in running LLMs.

The Contenders

Before we delve into the benchmark results, let's take a closer look at the hardware we'll be comparing:

1. Mac Mini M4: The latest addition to Apple's lineup, featuring 10 cores and 10 threads, with 16GB of RAM.
2. MacBook Pro with M3 Pro: Boasting 11 cores and 11 threads.
3. AMD Radeon RX 6700 XT: A powerful GPU with 12GB of VRAM.
4. NVIDIA GeForce RTX 3080 Ti: A high-end GPU with 12GB of VRAM, running in a virtual machine.

Benchmark Setup

For this comparison, we'll be using one of the best coding models available: the 7B version of CodeLlama. The installation process is straightforward, making it an ideal choice for our benchmark.

The Model: CodeLlama 7B

CodeLlama is a state-of-the-art language model specifically designed for coding tasks. Its 7B parameter version strikes a balance between performance and resource requirements, making it suitable for running on a variety of hardware configurations.

Benchmark Command

To run our benchmark, we'll use the following command:

llama run q1 2.5-h CodeLlama-7B --verbose

This command will load the model into memory (either GPU or CPU) and provide us with detailed performance metrics.

Performance Results

First Benchmark Run

After running the benchmark on all four systems, here are the results, ordered from fastest to slowest:

1. NVIDIA GeForce RTX 3080 Ti: ~120 tokens per second
2. AMD Radeon RX 6700 XT: ~46 tokens per second
3. MacBook Pro M3 Pro: ~27 tokens per second
4. Mac Mini M4: ~20 tokens per second

Second Benchmark Run

To ensure consistency, we performed a second benchmark run. The results were similar to the first run, confirming the reliability of our findings.

Analysis

Speed Comparison

The NVIDIA GeForce RTX 3080 Ti clearly takes the lead in raw performance, processing tokens at an impressive rate of around 120 per second. This is more than double the speed of its closest competitor, the AMD Radeon RX 6700 XT.

The Mac Mini M4, while being the slowest in this comparison, still manages a respectable 20 tokens per second. This performance is particularly noteworthy considering its compact form factor and lower power consumption.

Cost-Efficiency

When evaluating these results, it's essential to consider the cost of each system:

1. MacBook Pro M3 Pro: Over $2,000 USD
2. Mac Mini M4: $600 USD
3. NVIDIA GeForce RTX 3080 Ti: Varies (secondhand market prices can be competitive)
4. AMD Radeon RX 6700 XT: Varies

Taking price into account, the Mac Mini M4 emerges as a highly efficient option in terms of price-to-performance ratio. While it may not match the raw speed of dedicated GPUs, its affordability makes it an attractive choice for many users.

Power Consumption

One significant advantage of the M4 processor is its power efficiency. Using a power meter, we measured the Mac Mini M4's power consumption:

• Idle: ~3 watts
• Full load (running LLM): ~30 watts

This power efficiency is impressive compared to discrete GPUs like the NVIDIA RTX 3080 Ti or AMD Radeon RX 6700 XT, which typically consume around 200 watts or more under load.

Practical Implications

For Developers and Researchers

For those working with large language models, these benchmark results have several implications:

1. High-Performance Needs: If you require the absolute fastest processing of LLMs and have the budget for it, a high-end NVIDIA GPU like the RTX 3080 Ti is the way to go.

2. Balanced Performance: The AMD Radeon RX 6700 XT offers a good middle ground, providing solid performance at a potentially lower cost than top-tier NVIDIA options.

3. Efficiency and Portability: For users who prioritize energy efficiency, quiet operation, or need a compact setup, the Mac Mini M4 presents an attractive option. Its performance, while not top-tier, is sufficient for many LLM tasks.

4. Professional On-the-Go: The MacBook Pro with M3 Pro offers a good balance of performance and portability, making it suitable for professionals who need to work with LLMs while traveling.

Considerations for Different Use Cases

Home Labs and Small Offices

For individuals setting up home labs or small offices focused on AI and machine learning, the Mac Mini M4 could be an excellent choice. Its low power consumption and quiet operation make it ideal for environments where noise and heat are concerns.

Academic Research

Researchers working with LLMs might find the balance of performance and cost-effectiveness offered by the AMD Radeon RX 6700 XT appealing, especially if working with limited grant funding.

Professional AI Development

For companies and professionals engaged in intensive AI development, the high-performance NVIDIA GPUs remain the go-to choice, offering the fastest processing times for large models.

Cloud Computing Considerations

While this benchmark focuses on local hardware, it's worth noting that cloud computing solutions can offer flexibility in scaling resources up or down based on project needs. However, for consistent, long-term use, owning hardware can be more cost-effective.

Future Outlook

Potential for Apple Silicon

The performance of the Mac Mini M4, while not leading the pack, is impressive considering its price point and power efficiency. As Apple continues to refine its silicon, we may see future iterations closing the gap with dedicated GPUs in LLM processing tasks.

Advancements in GPU Technology

Both NVIDIA and AMD are continually pushing the boundaries of GPU performance. Future generations of GPUs are likely to offer even faster processing speeds for LLMs, potentially with improved energy efficiency.

Specialized AI Hardware

The growing importance of AI and machine learning may lead to more specialized hardware designed specifically for tasks like running LLMs. This could potentially offer better performance and efficiency than current general-purpose GPUs.

Optimizing LLM Performance

Regardless of the hardware you choose, there are several ways to optimize the performance of large language models:

1. Model Quantization: Using quantized versions of models can significantly reduce memory requirements and increase inference speed, albeit with a potential slight decrease in accuracy.

2. Efficient Prompt Engineering: Crafting efficient prompts can reduce the number of tokens processed, leading to faster overall performance.

3. Batching: Processing multiple inputs in batches can improve throughput, especially on GPUs.

4. Model Pruning: Removing unnecessary parameters from models can lead to faster processing times without significant loss in quality.

5. Hardware-Software Optimization: Ensuring that your software stack is optimized for your specific hardware can lead to significant performance improvements.

Conclusion

The benchmark comparison between the Mac Mini M4, MacBook Pro M3 Pro, AMD Radeon RX 6700 XT, and NVIDIA GeForce RTX 3080 Ti provides valuable insights into their performance in running large language models.

While the NVIDIA GPU stands out as the performance leader, each system has its strengths:

• The NVIDIA RTX 3080 Ti offers unmatched speed for those who need the absolute best performance.
• The AMD Radeon RX 6700 XT provides a strong balance of performance and potential cost-effectiveness.
• The MacBook Pro M3 Pro combines good performance with portability.
• The Mac Mini M4 shines in terms of energy efficiency and cost-effectiveness, making it an attractive option for many users.

Ultimately, the choice of hardware for running LLMs will depend on individual needs, balancing factors such as performance requirements, budget constraints, power efficiency, and form factor preferences. As the field of AI and machine learning continues to evolve, we can expect ongoing advancements in both hardware and software optimizations for running large language models.

Additional Resources

For those interested in diving deeper into the world of large language models and hardware optimization, here are some valuable resources:

1. Apple's M4 Chip Technical Specifications
2. NVIDIA GPU Computing Documentation
3. AMD ROCm Platform for GPU Computing
4. Hugging Face: Optimizing Transformer Models
5. PyTorch Performance Tuning Guide

By staying informed about the latest developments in hardware and software for LLMs, you can make informed decisions and optimize your workflow for maximum efficiency and performance.

Article created from: https://youtu.be/ayI5FVuEdu8?si=H-Fjijx6ndj4l-Ji