B200 GPU Rental

B200 features the revolutionary Blackwell architecture with 2.5x performance improvement for AI workloads. Key differences include: 192GB HBM3e memory (2.4x more than H100), 8 TB/s memory bandwidth (2.4x faster), 5th generation Tensor Cores with FP4 precision support, and enhanced Transformer Engine. B200 is specifically designed for trillion-parameter models and next-gen AI applications.

B200 GPUs are currently in limited availability with early access program. Spheron is working directly with major Data Center providers to secure allocation for our customers. Contact our team to discuss your requirements and timeline. Priority is given to large-scale training workloads and research institutions.

FP4 (4-bit floating point) is a new precision format introduced with Blackwell architecture. It enables 2x throughput compared to FP8 while maintaining model accuracy for inference workloads. This dramatically reduces cost per token for LLM inference and enables larger models to fit in memory. The 2nd-gen Transformer Engine automatically handles mixed FP4/FP8/FP16 precision.

Yes! B200 is specifically designed for trillion-parameter scale. With 192GB per GPU and NVLink switch providing 1.8 TB/s bandwidth, you can efficiently train models up to 2T+ parameters using distributed training frameworks like DeepSpeed, Megatron-LM, or FSDP. An 8-GPU B200 system provides 1.5TB of unified GPU memory.

All major frameworks have B200 support: PyTorch 2.2+, TensorFlow 2.15+, JAX 0.4.20+. NVIDIA provides Blackwell-optimized containers with CUDA 12.4, cuDNN 9.0, and framework-specific optimizations. Support includes new features like FP4 precision, enhanced Transformer Engine, and improved NCCL for multi-GPU scaling.

NVLink switch provides 1.8 TB/s bidirectional bandwidth per GPU (18x faster than PCIe Gen5), enabling GPUs to communicate directly without CPU bottlenecks. This is crucial for distributed training where gradient synchronization can be a major bottleneck. With 8 B200s connected via NVLink, you get near-linear scaling efficiency (90%+) even for largest models.

B200 GPUs cost $30,000-40,000 each when available for purchase, plus infrastructure costs (servers, cooling, power, networking). A single B200 on Spheron at $2.25/hr requires 5,600+ hours (233 days of continuous use) to match purchase cost. For most use cases, on-demand rental is significantly more cost-effective, especially for bursty workloads.

Absolutely! B200 provides exceptional inference performance with FP4 precision support, delivering up to 9,000 TFLOPS. It can serve very large models (100B+ parameters) with high throughput. However, for inference-only workloads under 70B parameters, you might find better cost-efficiency with H100 or A100 GPUs.

B200 excels at: trillion-parameter model training, very large LLM inference (100B+ params), multi-modal foundation models, mixture-of-experts architectures, high-resolution generative AI (video, 3D), and scientific computing requiring massive memory. If your model is under 100B parameters or fits comfortably in H100 memory, H100 or A100 may be more cost-effective.

Yes! For enterprise customers and research institutions, we offer dedicated B200 clusters with custom configurations (8-512 GPUs), reserved capacity, and volume pricing. Dedicated clusters include priority support, custom networking, and flexible billing. Contact our enterprise team to discuss your requirements.

Yes, Spheron offers Spot instances for B200 at significantly reduced rates (up to 70% savings). However, Spot instances can be interrupted when demand increases. Key risks include: potential job interruption during training/inference, loss of unsaved state or checkpoints, and need to restart from last saved checkpoint. Best practices: implement frequent checkpointing (every 15-30 minutes), use Spot for fault-tolerant workloads, save model weights to persistent storage regularly, and consider Spot for development/testing rather than production workloads. Given B200's premium nature and use for trillion-parameter models, we recommend dedicated instances for critical training runs to avoid losing days of compute progress.