Model Parallelism
Definition
A distributed training technique that splits a large AI model across multiple GPUs or machines, enabling training of models too large for a single device.
Why It Matters
Key Takeaways
- 1.Model Parallelism is a core concept for modern business and technology strategy
- 2.Practical application requires combining theory with data-driven experimentation
- 3.Understanding this concept helps teams make better technology and growth decisions
Real-World Examples
Applied model parallelism to achieve competitive advantages.
Growth Relevance
Model Parallelism directly impacts growth by influencing how companies acquire, activate, and retain customers.
Ehsan's Insight
Model parallelism — splitting a model across multiple GPUs — is necessary for models too large to fit in a single GPU's memory. An H100 has 80GB. A 70B parameter model in FP16 requires 140GB. You need at least 2 GPUs. Pipeline parallelism (splitting layers across GPUs) is simpler but creates pipeline bubbles (idle time). Tensor parallelism (splitting individual layers across GPUs) is more efficient but requires fast inter-GPU communication (NVLink). For most inference deployments, tensor parallelism across 2-4 GPUs on a single node is optimal. Cross-node parallelism introduces network latency that kills inference speed.
Ehsan Jahandarpour
AI Growth Strategist & Fractional CMO
Forbes Top 20 Growth Hacker · TEDx Speaker · 716 Academic Citations · Ex-Microsoft · CMO at FirstWave (ASX:FCT) · Forbes Communications Council