GB300 vs RTX 4080

FP16 performance defines training capabilities: the GB300's 2250 TFLOPS allows processing trillion-parameter LLMs with large batch sizes, while the RTX 4080's 48.7 TFLOPS limits it to smaller models or reduced batches. FP32 at 90 TFLOPS on GB300 supports general compute better than RTX 4080's 48.7 TFLOPS, though the gap narrows here.

For inference, FP8 precision shines on GB300 at 4500 TFLOPS, enabling high-throughput serving of massive models without quantization losses common on RTX 4080. Memory bandwidth of 12000 GB/s on GB300 sustains data flow for huge datasets, preventing stalls that 717 GB/s on RTX 4080 causes in memory-bound tasks.

VRAM disparity is critical: 288 GB HBM3e on GB300 accommodates full model loading for optimal batch sizes in training, versus 16 GB GDDR6X on RTX 4080 which demands model parallelism or sharding. TDP of 1400W for GB300 requires enterprise infrastructure, contrasting RTX 4080's efficient 320W for edge or small clusters.

Select the GB300 for large-scale LLM training or inference where 288 GB HBM3e VRAM holds entire models: its 2250 TFLOPS FP16 and 12000 GB/s bandwidth enable massive batches in NVLink clusters. Ideal for enterprises running FP8 workloads at 4500 TFLOPS in datacenters with SXM form factors and 1400W power budgets.

Opt for the RTX 4080 in cost-sensitive scenarios like prototyping or fine-tuning models under 16 GB VRAM: cloud pricing starts at $0.11 per hour with 48.7 TFLOPS FP16/FP32 performance. Suited to PCIe setups with 320W TDP for gaming, Stable Diffusion, or single-node inference where availability trumps raw scale.