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NVIDIA's GB300 NVL72 system demonstrates that overcoming communication bottlenecks in Mixture of Experts (MoE) architectures requires tightly coupled hardware-software co-design rather than just raw compute power. The achievement highlights how advances in non-blocking fabrics and software stacks like Megatron Core are essential for scaling frontier models without throughput degradation as GPU counts increase. This progress directly addresses the critical path challenges inherent to training massive parameter-scale AI systems using MoE structures.

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NVIDIA’s GB300 NVL72 Achieves 1,648 TFLOPs with DeepSeek-V3

Quantum ZeitgeistJuly 25, 20265 min read

NVIDIA’s GB300 NVL72 achieved 1,648 TFLOPs during DeepSeek-V3 671B pre-training, a new world record.

NVIDIA’s GB300 NVL72 has achieved 1,648 TFLOPs per GPU during pre-training of the DeepSeek-V3 671B model, setting a new performance record and demonstrating gains in AI training capability. Software innovations, including NVIDIA Megatron Core, TorchTitan, and JAX, have delivered three to ten times performance improvements over previous generations, with per-GPU throughput holding above 97% when scaling from 256 to 1,024 GPUs. This demonstrates the effectiveness of NVIDIA’s continuous hardware-software co-design and open-source contributions. The performance relies on advances across the entire AI platform, from silicon to networking to software, pushing training performance forward as frontier…

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