GB300 SXM6 vs Tesla T4

Raw compute reveals the GB300's supremacy: its 2250 TFLOPS FP16 performance dwarfs the T4's 8.1 TFLOPS, enabling faster model training where half-precision dominates. The FP32 rate of 90 TFLOPS on GB300 versus 8.1 TFLOPS on T4 accelerates single-precision tasks like simulations, while FP8 at 4500 TFLOPS suits quantized inference at scale. This delta translates to training large language models in hours rather than days on T4 equivalents. Memory bandwidth defines bottlenecks: 12000 GB/s on GB300 supports massive batch sizes for stable training gradients, unlike the T4's 320 GB/s which limits datasets to smaller scales and risks out-of-memory errors with modern models exceeding 16 GB VRAM. In inference, GB300 handles high-concurrency requests effortlessly, whereas T4 suits low-latency, low-volume serving. Overall, GB300 excels in throughput-heavy AI pipelines.

Opt for the GB300 in large-scale LLM training or fine-tuning where 288 GB HBM3e VRAM accommodates models over 100B parameters without sharding. Its 12000 GB/s bandwidth and 2250 TFLOPS FP16 enable efficient handling of trillion-parameter workloads across NVLink clusters. Data centers prioritizing peak performance over power draw select GB300 for scientific computing simulations demanding 90 TFLOPS FP32.

The T4 shines in budget-conscious inference for deployed models under 16 GB, with cloud pricing starting at $0.53 per hour across six providers. Its 70W TDP fits edge servers or virtualized environments without cooling overhauls, delivering 8.1 TFLOPS FP16 for real-time tasks like video analytics. Legacy PCIe setups retain T4 for cost averaging $1.66 per hour versus unavailable GB300 offers.