Layer 4: Custom Silicon — Meta’s Path to Breaking NVIDIA Dependency

The Problem: NVIDIA Dependency

NVIDIA (H100/B200) controls ~80% of the market. Everyone depends on them: Meta, Google, Microsoft, Amazon, OpenAI, xAI, Anthropic…

The Risks

• Supply constraints = strategic vulnerability
• Pricing power remains with Nvidia
• Competing for same limited allocation
• Dependency = existential risk in AI race

Meta’s Solution: Build Your Own

MTIA (Meta Training and Inference Accelerator)

• Custom inference silicon
• Optimized for Meta’s specific workloads
• Focus: inference, not training
• Better perf/watt for recommendations
• Multiple generations in production

Training still on Nvidia (for now)

Rivos Acquisition (~$1B reported)

• RISC-V expertise (open architecture)
• ~80 chip engineers
• Custom silicon design capability
• Path beyond x86/ARM licensing

Accelerating the timeline

The Phased Approach

The Strategic Logic

Meta doesn’t need to beat Nvidia. It needs optionality. Custom silicon for inference (where Meta’s workloads live) reduces dependency enough to negotiate from strength — and ensures survival regardless of GPU allocation politics.

This is part of a comprehensive analysis. Read the full analysis on The Business Engineer.