Layer 2 · head to head
Meta MTIA v2 vs NVIDIA B200
The custom-silicon question in one line: every MTIA Meta deploys is a B200 NVIDIA does not sell.
| Meta MTIA v2 | NVIDIA B200 | |
|---|---|---|
| Can you get it | Not available to anyone outside the company that built it. | Rentable by the hour from multiple clouds; purchasable at scale. |
| Memory | Not published | 192 GB HBM3E |
| Bandwidth | Not published | 8 TB/s |
| Compute | Not published | FP4 and FP8 sparse; vendor figures vary by configuration |
| Power | Not published | Up to 1,000 W per GPU in liquid-cooled configurations |
| Interconnect | Not published | NVLink 5 |
| Software | Internal, PyTorch-based | CUDA |
| Workload | inference | both |
Meta MTIA v2
For: Nobody outside Meta. It is in this table because leaving it out would misrepresent the market — hyperscaler custom silicon collectively outweighs AMD as a threat to NVIDIA.
The catch: You cannot rent, buy or benchmark it. Its market effect is entirely in the GPU orders Meta does not place.
Economics: Relevant to you only as demand it removes from the market you buy in.
NVIDIA B200
For: The default frontier accelerator. If you have no specific reason to choose otherwise, this is what the market chose.
The catch: 192 GB is less memory than AMD's MI355X at 288 GB, and for memory-bound inference that gap is the whole argument.
Economics: CUDA maturity is the real product. The chip is competitive; the software moat is why it wins procurement.
Before either — can you power it?
Not published against Up to 1,000 W per GPU in liquid-cooled configurations. In 2026 that comparison usually matters more than the FLOPS one: the US interconnection queue exceeds 2,600 GW with waits approaching five years, and roughly 80% of projects withdraw before energising.
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