B300 GPU Rental

The B300 is NVIDIA's Blackwell Ultra generation GPU, the successor to the B200. Key improvements include: 288GB HBM3e memory (50% more than B200's 192GB), 10 TB/s memory bandwidth (25% faster), enhanced Tensor Core throughput (~33% uplift across precision formats), and higher TDP for sustained peak performance. It is purpose-built for frontier-scale AI training and ultra-large-scale inference.

B300 GPUs are in limited early availability. Spheron is working directly with Tier 3/4 data center partners to secure allocation. Contact our team to discuss your requirements and reserve capacity, priority is given to large training runs and research institutions.

288GB per GPU matters when fitting the full model or optimizer state in GPU memory is a constraint at B200's 192GB. Prime examples: trillion-parameter dense transformer training without model parallelism, inference serving of 200B+ parameter models on a single GPU, very long context windows (500K–1M tokens), and large-scale reinforcement learning with huge replay buffers.

Yes. For inference, B300 excels at models that don't fit on B200 (200B+ parameters) and high-throughput serving where memory bandwidth is the bottleneck. For models under 100B parameters, B200 or H100 may offer better cost efficiency. The B300's FP4 support (12,000 TFLOPS) is exceptional for quantized inference of very large models.

All major frameworks are supported: PyTorch 2.3+, TensorFlow 2.16+, JAX 0.4.25+. NVIDIA provides Blackwell Ultra-optimized containers with CUDA 12.5+, cuDNN 9.1+, and TensorRT 10.1+. Framework-level support for FP4 precision, enhanced Transformer Engine, and improved NCCL collective operations is available out-of-the-box.

A single B300 delivers approximately 3.3x H100 training throughput and 3.6x the memory. For workloads that fit on B200/H100, multiple H100s may be more cost-effective. But for workloads requiring >192GB VRAM or extreme bandwidth (10 TB/s), B300 eliminates inter-node communication overhead and simplifies deployment significantly.

NVIDIA B300 GPUs are expected to cost $40,000–$50,000 per card at availability, plus server, cooling, networking, and power costs. At $3.50/hr on Spheron, it would take 5,700+ hours (over 7 months of continuous use) to match the hardware acquisition cost, before factoring in data center infrastructure. For most teams, on-demand rental is dramatically more economical.

Yes. For enterprise customers and research labs requiring sustained access, we offer reserved B300 capacity and dedicated clusters (8–256 GPUs) with custom networking and volume pricing. Contact our enterprise team for more details.

Spheron provides bare-metal B300 access from Tier 3/4 data centers, meaning no hypervisor overhead, direct NVLink configuration, and significantly lower pricing (often 2–6x cheaper than AWS/Azure/GCP). Deployment is faster, billing is per-minute, and there are no long-term contracts. You get the full GPU, not a virtualized slice.

Yes, Spot instances for B300 are available at reduced rates (up to 60% savings). Given B300's use for critical large training runs, we strongly recommend implementing checkpointing every 15–30 minutes, saving model weights to persistent storage frequently, and using Spot for development and testing. For production trillion-parameter training jobs, dedicated instances eliminate the risk of losing days of compute progress.