B200 NVL vs RTX 4080

The B200's 192 GB HBM3e VRAM dwarfs the RTX 4080's 16 GB GDDR6X, enabling the handling of large language models without extensive model parallelism. This capacity supports training and inference on models exceeding 100 billion parameters intact. In contrast, the RTX 4080 limits users to smaller models or requires techniques like quantization.

Bandwidth of 8000 GB/s on the B200 permits batch sizes up to 10 times larger than the RTX 4080's 717 GB/s, accelerating throughput in training loops and reducing per-iteration latency. FP16 performance at 4500 TFLOPS on the B200 delivers approximately 92 times the tensor compute of the RTX 4080's 48.7 TFLOPS, slashing training times for deep learning. FP32 at 90 TFLOPS remains superior to 48.7 TFLOPS, benefiting simulations; FP8 at 9000 TFLOPS optimizes inference efficiency.

Power draw reflects scale: the B200's 1000W TDP demands robust cooling versus the RTX 4080's 320W, impacting deployment costs in dense clusters.

The B200 excels in large-scale AI training and inference where 192 GB VRAM handles full models without sharding. Its 4500 TFLOPS FP16 and 8000 GB/s bandwidth enable rapid iteration on trillion-parameter LLMs, justifying $10.50 per hour pricing for enterprises. NVLink and PCIe 6.0 interconnects facilitate multi-GPU scaling in NVL form factors.

The RTX 4080 suits budget-conscious prototyping, fine-tuning small models, and creative tasks like Stable Diffusion, where 16 GB VRAM and 48.7 TFLOPS FP16 suffice. At $0.11 per hour starting price, it offers accessibility for individuals or teams testing ideas before scaling. PCIe form factor simplifies integration in standard cloud instances.