NVIDIA B200 GPU Outpaces Predecessors in LLM Tests
Recent performance benchmarks for large language model inference workloads show NVIDIA’s new Blackwell B200 architecture significantly outperforms its H200, H100, and RTX PRO 6000 GPUs. The tests reveal that compute and memory bandwidth are becoming critical bottlenecks for AI infrastructure. The results position the B200 as the leading hardware for demanding AI development.
- The Blackwell B200 architecture is built on a dual-die design, effectively combining two chips to function as a single, unified GPU. This design packs 208 billion transistors, a significant increase from the 80 billion found on the previous generation H100 and H200 GPUs. - The B200 utilizes HBM3e memory, offering a substantial increase in both capacity and bandwidth. It features 192 GB of HBM3e memory with a bandwidth of 8 TB/s, compared to the H100's 80 GB and 3.35 TB/s. - For AI inference tasks, the B200 introduces support for new, lower-precision data formats, including FP4, which allows it to achieve up to 30 times faster performance compared to the H100. In AI training, it demonstrates up to 4 times the performance of its predecessor. - The B200 is a key component of the GB200 Grace Blackwell Superchip, which pairs two B200 GPUs with a Grace CPU. This integration is designed to eliminate data transfer bottlenecks between the CPU and GPUs. - To facilitate communication between GPUs, the B200 uses the fifth generation of NVLink, which provides 1.8 TB/s of bidirectional bandwidth per GPU. This is double the bandwidth of the NVLink used in the H100. - The power consumption of the B200 is rated at 1,000W per GPU, a notable increase from the 700W TDP of the H100 and H200, necessitating more robust cooling solutions, often liquid-based. - NVIDIA's system-level design, the GB200 NVL72, connects 72 Blackwell GPUs within a single liquid-cooled rack, functioning as a massive, single GPU to handle trillion-parameter language models.
