NVIDIA B300 vs NVIDIA B200
Specs, monthly cost in taka and the workloads each GPU suits, side by side.
Tensor figures are NVIDIA's published numbers with sparsity (dense is half). Source: B300, B200.
The short answer
Pick the NVIDIA B300 if: You work with the largest models, very long context or reasoning workloads that need 288 GB on a single GPU.
Pick the NVIDIA B200 if: You train large LLMs or need the fastest inference available, with 180 GB per GPU — 70B in FP16 fits on one card.
The NVIDIA B300 costs about 14% more per hour than the NVIDIA B200. If your model fits comfortably on the cheaper GPU and you are not short of time, the cheaper one usually wins. Compare with your own model →
Common questions
Which is faster, the NVIDIA B300 or the NVIDIA B200?
Both have the same tensor compute (4,500 TFLOPS). The difference is memory and memory bandwidth (8 TB/s vs 8 TB/s), which speeds up memory-bound inference on large models.
Which one costs less?
The NVIDIA B200 is ৳1,652 per hour and the NVIDIA B300 is ৳1,880, about 14% more. For 40 hours that is ৳66,080 vs ৳75,200.
Which has more memory?
The NVIDIA B300 has 288 GB and the NVIDIA B200 has 180 GB. Roughly, the NVIDIA B300 fits 70B in FP16 with long context, about 120B in FP16; the NVIDIA B200 fits 70B in FP16.
Which should I choose for my project?
Choose the NVIDIA B300 if: You work with the largest models, very long context or reasoning workloads that need 288 GB on a single GPU. Choose the NVIDIA B200 if: You train large LLMs or need the fastest inference available, with 180 GB per GPU — 70B in FP16 fits on one card.