Comparing Local Machine Learning Models: Intel vs Apple vs NVIDIA

Introduction

Running machine learning models locally has become increasingly important for those who want to avoid ongoing cloud service costs. This article compares the performance of various consumer hardware options for running local machine learning models, focusing on Intel, Apple, and NVIDIA solutions.

Hardware Setup

The comparison includes the following hardware:

• Intel NUC-style box with a Core Ultra 5 processor and 96GB of RAM
• Apple M2 Pro Mac Mini
• Mini PC with an RTX 4090 GPU connected via OCuLink

While this covers a range of options, it's worth noting that it doesn't include high-end solutions like the Mac Studio with M2 Ultra chip. The RTX 4090 setup is limited to 24GB of VRAM, which restricts the size of models that can be run efficiently.

Benchmark Methodology

The comparison uses a 7 billion parameter language model to ensure compatibility across all tested hardware. The benchmark includes:

1. Power consumption measurements
2. Speed tests for generating 1000-word stories
3. The open-source LLM Benchmark tool

Power Consumption

Idle Power Draw

• RTX 4090 setup: 62 watts
• Mac Mini: 15 watts
• Intel NUC: 17 watts

Power Draw During Model Running

• RTX 4090 setup: Up to 320 watts
• Mac Mini: 45-57 watts
• Intel NUC: 49-66 watts

Speed Test Results

1000-word Story Generation

1. RTX 4090: Fastest completion
2. M2 Pro Mac Mini: Second fastest
3. Intel NUC: Slowest completion

The Intel NUC was tested using both CPU-only and GPU-accelerated configurations, with the GPU version showing improved performance.

LLM Benchmark Results

Mac Mini (M2 Pro)

• 7-8B parameter models: 32-50 tokens per second
• 9B parameter model: 25 tokens per second
• 13B parameter model: 20 tokens per second

RTX 4090

• 7-8B parameter models: 130-200 tokens per second
• 9-13B parameter models: ~90 tokens per second

The Intel NUC took significantly longer to complete the benchmark, with results not available at the time of writing.

Performance Analysis

Total Energy Consumption

Based on average power draw and completion time:

1. M2 Pro Mac Mini: Lowest energy consumption
2. RTX 4090: Second lowest
3. Intel NUC: Highest energy consumption

Time to First Token

• Intel NUC and M2 Pro Mac Mini: 1 second
• RTX 4090: 2 seconds

The RTX 4090's longer time to first token is due to the need to copy the model from system RAM to VRAM.

Performance per Watt

The M2 Pro Mac Mini demonstrated the best performance per watt among the tested systems.

Use Case Considerations

Coding Assistance

For tasks requiring fast, accurate responses (e.g., coding assistance), the Intel NUC and Mac Mini may be preferable due to their quicker response times for short queries.

Large Text Generation

For generating large blocks of text, the RTX 4090's superior speed becomes more apparent and beneficial.

Environmental Factors

Heat Generation

1. M2 Pro Mac Mini: Minimal heat output
2. Intel NUC: Moderate heat output
3. RTX 4090: Highest heat output

Noise Levels

1. M2 Pro Mac Mini: Silent operation
2. Intel NUC: Moderate noise
3. RTX 4090: Loudest operation

Cost Analysis

Hardware Costs

1. Intel NUC: $500-$600
2. M2 Pro Mac Mini: $1,100
3. RTX 4090 setup: $2,600-$2,700 (Mini PC, OCuLink dock, RTX 4090, power supply)

Operational Costs

Assuming 200 calculations per day and average U.S. electricity prices:

1. Intel NUC: Lowest annual cost
2. M2 Pro Mac Mini: Second lowest annual cost
3. RTX 4090: Highest annual cost

In countries with higher electricity costs (e.g., Denmark, Germany), the operational cost difference becomes even more significant.

Conclusion

Each system has its strengths and weaknesses:

• The M2 Pro Mac Mini offers the best balance of performance, energy efficiency, and cost-effectiveness.
• The RTX 4090 setup provides the highest raw performance for large-scale text generation but at the cost of higher power consumption and operational expenses.
• The Intel NUC, while less powerful, offers a budget-friendly option for those with less demanding requirements.

The choice between these systems depends on individual needs, considering factors such as performance requirements, budget constraints, and environmental considerations.

Future Outlook

As technology continues to evolve, we can expect improvements in both performance and efficiency. Future iterations of Intel's hardware, such as the upcoming Lunar Lake series, promise enhanced efficiency. Similarly, advancements in GPU technology and Apple's silicon may further shift the balance in local machine learning capabilities.

Practical Implications

For Developers and Researchers

Developers and researchers working with machine learning models should consider:

1. The size of models they typically work with
2. The frequency and duration of model runs
3. The importance of quick response times vs. overall processing speed
4. Budget constraints for both initial hardware costs and ongoing operational expenses

For Businesses

Businesses implementing local machine learning solutions should evaluate:

1. The scalability of different hardware solutions
2. The total cost of ownership, including hardware, energy, and cooling costs
3. The potential impact on office environment (heat and noise)
4. The alignment of hardware capabilities with specific use cases (e.g., real-time processing vs. batch processing)

For Home Users

Individuals interested in running machine learning models at home should consider:

1. The types of projects they plan to work on
2. Their tolerance for noise and heat generation
3. The impact on their electricity bills
4. The initial investment required for different hardware options

Best Practices for Local Machine Learning Setups

1. Optimize model size: Choose the smallest model that meets your accuracy requirements to improve speed and reduce resource usage.
2. Implement efficient cooling: Proper ventilation and cooling can help maintain performance and extend hardware lifespan.
3. Monitor power consumption: Use power monitoring tools to track energy usage and optimize workloads.
4. Leverage hardware-specific optimizations: Take advantage of libraries and frameworks optimized for specific hardware (e.g., Intel's IPEX LLM for Arc GPUs).
5. Consider hybrid approaches: Combine local processing with cloud services for the best balance of performance and cost-effectiveness.

The Future of Local Machine Learning

As machine learning becomes more prevalent in various applications, the demand for efficient local processing solutions is likely to grow. We can expect:

1. More energy-efficient hardware designs
2. Improved software optimizations for different hardware architectures
3. The development of specialized AI accelerators for consumer devices
4. Increased focus on edge computing solutions that balance performance and power efficiency

By staying informed about these developments and carefully evaluating hardware options, users can make informed decisions about their local machine learning setups, balancing performance, cost, and energy efficiency to meet their specific needs.

Article created from: https://youtu.be/0EInsMyH87Q?si=f_8fcP7C-uWwtwlR