Rent NVIDIA B300 in the Cloud

For a NVIDIA B300 hosted instance, plan around per hour as the entry point — that is currently the budget-friendly on-demand rate, available from . If your model training pipeline is checkpointable, spot pricing pulls the bill down to per hour on (~up to 60% discount vs. on-demand).

A full month of continuous NVIDIA B300 time at on-demand rates lands in the low-to-mid three figures in USD, which is why sustained production workloads often move to reserved contracts offering roughly up to 40% off. Serverless and per-second billing on supporting providers let you avoid the cost of idle GPU time altogether.

See the NVIDIA B300 page for the full spec sheet and current provider list.

Hardware summary for NVIDIA B300: architecture Blackwell Ultra, VRAM 288 GB HBM3e, bandwidth 8,000 GB/s, FP16 2,250 TFLOPS, FP32 75 TFLOPS, TDP 1,400W, year 2025.

Those specs cluster NVIDIA B300 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.

See the NVIDIA B300 page for the full spec sheet and current provider list.

NVIDIA B300 pushes 2,250 TFLOPS of FP16, 75 TFLOPS of FP32, and feeds them from 288 GB of VRAM at 8,000 GB/s.

Benchmarks: LLM training with mixed precision sees near-peak FLOPS utilisation at batch sizes that fit in VRAM; LLM inference is typically within 5-15% of the theoretical bandwidth-bound ceiling on autoregressive decoding; diffusion models show the biggest jump over older accelerators, where faster attention kernels stack with the raw compute gains.

See the NVIDIA B300 page for the full spec sheet and current provider list.

To run NVIDIA B300, you have 1 cloud providers to choose from: DigitalOcean. The lowest-cost currently is at per hour on-demand, with leading on spot at per hour.

Our recommendation workflow: 1) eliminate providers without the region, compliance, or interconnect you require; 2) compare on-demand prices among the survivors; 3) add spot/reserved options for matching workloads; 4) check egress and minimum-commitment policies before signing up.

See the NVIDIA B300 page for the full spec sheet and current provider list.

NVIDIA B300 was designed for Frontier AI training, largest model workloads. In cloud deployments that translates to: AI model training at research-to-production scale, fine-tuning transformers, running batch serving pipelines, and accelerating diffusion/vision workloads.

It sits in the middle of the AI accelerator market — more capable than consumer cards, more affordable than frontier accelerators. For most teams, that's exactly the right tier: enough performance for real work, enough savings to run multiple experiments in parallel. Cloud access from per hour via .

See the NVIDIA B300 page for the full spec sheet and current provider list.