Rent NVIDIA B100 in the Cloud

Hardware summary for NVIDIA B100: architecture Blackwell, VRAM 192 GB HBM3e, bandwidth 8,000 GB/s, FP16 1,750 TFLOPS, FP32 60 TFLOPS, TDP 700W, year 2024.

Those specs cluster NVIDIA B100 firmly in the modern generation of AI accelerators. Whether it's the right fit depends on whether your bottleneck is capacity (VRAM), throughput (bandwidth or TFLOPS), or cost — all three matter more than any single headline number.

Full specs, benchmarks, and comparisons are on the NVIDIA B100 page.

Benchmarked performance on NVIDIA B100: 1,750 TFLOPS in FP16, 60 TFLOPS in FP32, 8,000 GB/s memory bandwidth, 192 GB VRAM.

For the workloads most engineers care about — model training transformer-family models, serving LLM low-latency inference, running diffusion and vision pipelines — those specs are enough to sustain batch sizes that keep tensor cores busy. Expect wall-clock gains versus previous-generation Blackwell cards to range from 1.5x to 3x depending on workload shape.

See the NVIDIA B100 page for the full spec sheet and comparisons to related GPUs.

NVIDIA B100 is best for workloads where its 192 GB VRAM and Blackwell tensor cores are well-matched: AI training, large-scale inference.

If your workload needs significantly more memory (e.g., training frontier-scale models from scratch), NVIDIA B100 is undersized and you'd want an H100/H200/B200 class card. If your workload needs less (e.g., small-scale serving on 7B-parameter models), cheaper cards like L4 or RTX 4090 may be more cost-efficient. For the middle band, NVIDIA B100 is usually the sensible pick.

See the NVIDIA B100 page for the full spec sheet and comparisons to related GPUs.