considering buying hardware to run everything locally. Would should I buy? #asknostr
https://www.youtube.com/watch?v=n43mPIT-7Co
Here's a summary of the YouTube video comparing the performance of the DeepSeek R1 14B model on an Apple M4 Mac Mini versus a Dell R250 system:
**Video Overview**
The video compares the performance of the DeepSeek R1 14B model running on an Apple M4 Mac Mini (10-core CPU, 10-core GPU, 16 GB unified memory) against a Dell R250 system equipped with an NVIDIA RTX A100 8 GB GPU. The presenter, Jamie Goodier from Savar Labs, runs benchmarks using the Ollama benchmark tool to evaluate token generation speeds for various models.
---
**Key Findings**
**1. Model Performance on Apple M4 Mac Mini**
• Llama 3.2: 42.2 tokens/sec
• Mistral: 23 tokens/sec
• DeepSeek R1 Models:
- 1.5B: ~80 tokens/sec
- 8B: ~19.1 tokens/sec
- 14B: ~11 tokens/sec
**2. Comparison with Dell R250 System**
• The Dell system (NVIDIA RTX A100 8 GB) generally outperforms the Apple M4 Mac Mini in raw throughput for smaller models.
• However, the Apple M4 Mac Mini shows slightly better performance for the DeepSeek R1 14B model due to its unified memory architecture, which allows it to fully load the model into memory (16 GB).
---
**Efficiency Considerations**
• Power Consumption: The Dell R250 system, being a rack-mounted unit with additional hardware (extra GPU, RAM), consumes more electricity than the compact Apple M4 Mac Mini.
• Cost: The Dell system is more expensive to purchase and configure compared to the Mac Mini.
• Use Case: The Apple M4 Mac Mini is a fun and efficient system for running smaller models, while the Dell system excels in raw throughput for smaller models.
---
**Conclusion**
The Apple M4 Mac Mini is a capable system for running LLMs, especially leveraging its unified memory to handle larger models like the DeepSeek R1 14B. However, the Dell R250 system with an NVIDIA RTX A100 still leads in raw performance for smaller models. The choice between the two depends on whether you prioritize raw speed, power efficiency, or cost-effectiveness.
https://t.me/BAopenbot
Here's a summary of the YouTube video comparing the performance of the DeepSeek R1 14B model on an Apple M4 Mac Mini versus a Dell R250 system:
**Video Overview**
The video compares the performance of the DeepSeek R1 14B model running on an Apple M4 Mac Mini (10-core CPU, 10-core GPU, 16 GB unified memory) against a Dell R250 system equipped with an NVIDIA RTX A100 8 GB GPU. The presenter, Jamie Goodier from Savar Labs, runs benchmarks using the Ollama benchmark tool to evaluate token generation speeds for various models.
---
**Key Findings**
**1. Model Performance on Apple M4 Mac Mini**
• Llama 3.2: 42.2 tokens/sec
• Mistral: 23 tokens/sec
• DeepSeek R1 Models:
- 1.5B: ~80 tokens/sec
- 8B: ~19.1 tokens/sec
- 14B: ~11 tokens/sec
**2. Comparison with Dell R250 System**
• The Dell system (NVIDIA RTX A100 8 GB) generally outperforms the Apple M4 Mac Mini in raw throughput for smaller models.
• However, the Apple M4 Mac Mini shows slightly better performance for the DeepSeek R1 14B model due to its unified memory architecture, which allows it to fully load the model into memory (16 GB).
---
**Efficiency Considerations**
• Power Consumption: The Dell R250 system, being a rack-mounted unit with additional hardware (extra GPU, RAM), consumes more electricity than the compact Apple M4 Mac Mini.
• Cost: The Dell system is more expensive to purchase and configure compared to the Mac Mini.
• Use Case: The Apple M4 Mac Mini is a fun and efficient system for running smaller models, while the Dell system excels in raw throughput for smaller models.
---
**Conclusion**
The Apple M4 Mac Mini is a capable system for running LLMs, especially leveraging its unified memory to handle larger models like the DeepSeek R1 14B. However, the Dell R250 system with an NVIDIA RTX A100 still leads in raw performance for smaller models. The choice between the two depends on whether you prioritize raw speed, power efficiency, or cost-effectiveness.
https://t.me/BAopenbot