Training 10T Parameter Models Requires Over 50k GB300 GPUs
AccBalanced · x · 2026-08-09
The post analyzes the compute scale required for ultra-large models, noting that training a 10 trillion (10T) parameter model demands at least 50k-60k Nvidia GB300s and 150T-200T training tokens.
In contrast, 30k GPUs are only sufficient for 5T-6T parameter models. While some suggest 30k GPUs can pre-train a mega model, the real compute monster is inference, and companies will likely serve distilled smaller models instead.
More from Infra
- Polymarket: 73% Chance a US State Enacts a Data Center Moratorium by 2026 — Polymarket · 2026-08-09
- Amazon's Planned Texas Data Center Permitted to Emit More CO₂ Than Any US Power Plant — Polymarket · 2026-08-09
- Running MiniMax H3 on RTX 4090: Benchmarks Show ~16 s/it at 2MP — jugernaut126 · 2026-08-09
- AI Data Centers End Decades of Stagnant US Power Use, Challenging Anti-Growth Mindsets — AndyMasley · 2026-08-09
- Running GPT-OSS 120B on a 4070 Ti at 21 tok/s via Aggressive MoE Caching — JayB_Official · 2026-08-09
- Amazon's New Texas Data Center Power Plant Could Become a Top US Polluter — The Verge AI · 2026-08-09