GPU Computing
Definition
Using graphics processing units for AI workloads, leveraging their parallel processing capabilities for faster model training and inference.
Why It Matters
Key Takeaways
- 1.GPU Computing is a foundational concept for modern business strategy
- 2.Understanding this helps teams make better technology and growth decisions
- 3.Practical application requires combining theory with data-driven experimentation
Real-World Examples
Applied gpu computing to achieve significant competitive advantages in their markets.
Growth Relevance
GPU Computing directly impacts growth by influencing how companies acquire, activate, and retain customers in an increasingly competitive landscape.
Ehsan's Insight
GPU computing is the new oil — critical to AI, controlled by a near-monopoly (NVIDIA has 80%+ market share for AI GPUs), and subject to geopolitical constraints (export controls to China). The H100 GPU costs $30K+ and has a 6-month wait time. This supply constraint shapes the entire AI industry: only companies with GPU access can train frontier models. For AI application companies, the GPU constraint is less severe because inference can run on cheaper hardware — A100s, L40s, or even CPUs for small models. The strategic play for startups: do not compete on model training (you cannot afford the GPUs). Compete on model application (you can run inference on accessible hardware). Let OpenAI and Google spend billions on training. Build on their APIs.
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