B300 vs GH200

Superior compute defines the B300's edge over GH200: its 2250 TFLOPS FP16 exceeds GH200's 1979 TFLOPS by 14 percent, accelerating deep learning training where mixed precision dominates. FP32 performance at 90 TFLOPS surpasses 67 TFLOPS by 34 percent, benefiting inference and simulations needing single-precision accuracy. FP8 throughput of 4500 TFLOPS tops 3958 TFLOPS, ideal for high-volume quantized inference.

Memory capacity and bandwidth transform practical workloads. B300's 288 GB HBM3e supports models three times larger than GH200's 96 GB limit, enabling trillion-parameter LLMs without excessive sharding. The 12000 GB/s bandwidth triples GH200's 4000 GB/s, sustaining larger batch sizes and reducing I/O bottlenecks during gradient computations.

Power draw reflects these gains: B300's 1200W TDP versus GH200's 900W indicates higher sustained performance in NVSwitch/NVLink clusters, though it demands robust cooling. Interconnects like NVLink-C2C on GH200 aid CPU-GPU cohesion, but B300's NVSwitch scales multi-GPU better for massive jobs.

The B300 suits extreme-scale AI where memory constraints bind progress. Its 288 GB HBM3e and 12000 GB/s bandwidth handle massive LLMs during training, supporting batch sizes infeasible on GH200's 96 GB and 4000 GB/s. Researchers pushing state-of-the-art models prioritize 2250 TFLOPS FP16 despite $6.94 per hour pricing.

Deployments leveraging FP8 inference at 4500 TFLOPS benefit from B300's capacity for serving huge models at low latency.

The GH200 fits cost-conscious operations with solid performance. At $1.99 per hour, it delivers 1979 TFLOPS FP16 for fine-tuning or inference on models under 96 GB HBM3, avoiding B300's $6.94 premium. Lower 900W TDP enables denser racks versus 1200W.

Established Hopper ecosystem supports quick scaling via NVLink-C2C and PCIe 5.0 for hybrid CPU-GPU tasks.