"INTELLECT-1 Technical Report"

Distributed LLM training now possible on regular internet, no supercomputer needed

INTELLECT-1 introduces a breakthrough in distributed LLM training by enabling collaborative training across global nodes using standard internet connections. The system trained a 10B parameter model across 30 independent compute providers spanning 3 continents while maintaining 83-96% compute efficiency.

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https://arxiv.org/abs/2412.01152

🌍 Original Problem:

Training LLMs traditionally requires high-bandwidth data center connections. Standard internet connections are 1000x slower, making distributed training across global nodes seemingly impossible.

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🔧 Solution in this Paper:

→ The PRIME framework enables fault-tolerant training across unreliable, globally distributed nodes through ElasticDeviceMesh technology.

→ DiLoCo algorithm combined with int8 quantization reduces communication bandwidth by 400x while maintaining model quality.

→ Hybrid approach uses FSDP for efficient local training and DiLoCo for minimal cross-node communication.

→ Dynamic node management system handles nodes joining/leaving through peer-to-peer checkpoint transmission.

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💡 Key Insights:

→ Global distributed training is viable with standard internet connections

→ Int8 quantization of pseudo-gradients is more robust than weight quantization

→ Blocking synchronization provides better training stability than non-blocking approaches

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📊 Results:

→ Achieved 83% compute utilization globally, 96% within USA

→ Trained 10B parameters across 112 H100 GPUs

→ Nodes run independently for 38 minutes before 1-7 minute synchronization

→ Maintained convergence despite severe bandwidth constraints