M4 Mac Mini CLUSTER 🤯
AI Summary
Video Summary: Machine Learning on Apple Silicon vs. GPUs
- Machine Learning Parallelism:
- CPUs are not efficient for parallel tasks.
- GPUs excel at parallel processing, hence their use in machine learning.
- RTX 490 GPUs are fast but expensive and power-hungry.
- Apple Silicon as an Alternative:
- Apple silicon offers a cost-effective solution for running local large language models (LLMs).
- A MacBook Pro with Apple silicon is cheaper initially and for ongoing costs compared to multiple RTX 490s.
- Memory Considerations:
- Larger models require more memory.
- Apple’s unified memory allows CPU and GPU to share the same RAM.
- Example: Mac mini with 64 GB RAM vs. Nvidia’s 24 GB.
- Machine Learning Frameworks:
- TensorFlow and PyTorch are common frameworks.
- Apple released MLX in 2023, optimized for Apple silicon, outperforming PyTorch in benchmarks.
- Setting Up a Cluster:
- The video explores whether clustering machines is faster for running models and if it increases the capability to run larger models.
- Clustering involves setup and tuning, with EXO being an easy-to-use tool for distributed computing.
- Experiment Setup:
- Various Mac mini models with different specs were used.
- Tested configurations: two base models vs. one model with double the RAM.
- Machines are connected via Thunderbolt bridge for faster performance compared to Wi-Fi or LAN.
- Manual IP configuration and jumbo packets were set up for networking.
- Performance Testing:
- Smaller models run efficiently on single machines.
- Direct Thunderbolt connections between machines improve performance.
- Memory bandwidth, not size, is crucial for token generation speed.
- Clustering two base models performed worse than a single M4 Pro model for larger models.
- EXO has overhead; running MLX directly is faster on a single machine.
- Power usage is low even when all machines are at full capacity.
- Heat management is a concern with clustered machines.
- Model Capacity:
- Base model Mac mini can run a 7 billion parameter model.
- A 32 billion parameter model is slow on two base Mac Minis but better on one M4 Pro Mac Mini.
- The largest tested model, Neotron 70b, is slow even on the most powerful machines.
- Final Thoughts:
- Clustering Macs has benefits, such as unified memory allowing for larger models.
- The cost-effectiveness of Apple silicon makes it an attractive option for those without the budget for high-end GPUs.
- The experiment shows potential but is not yet superior to a single powerful machine.
- Future videos will explore the performance of a MacBook Pro with 128 GB RAM.
- The setup has potential uses, especially with multiple 64 GB machines.
- Project EXO is worth following for updates on distributed computing with Apple silicon.
- URLs Mentioned:
- No URLs provided in the transcript.