B200 SXM on Cirrascale
Visit CirrascaleCirrascale's NVIDIA B200 SXM offering delivers enterprise-grade Blackwell architecture GPUs with 192GB HBM3e VRAM on bare-metal, non-virtualized servers, optimized for deep learning and HPC research. This combination stands out for research teams running extended training jobs on massive models, providing consistent, interference-free multi-GPU performance without virtualization overhead. Key value propositions include dedicated hardware access, ensuring predictable scaling across NVIDIA B200s via NVLink, and Cirrascale's diverse stack spanning Qualcomm, AMD, and NVIDIA accelerators for flexible experimentation. Monthly billing aligns with long-term workloads, minimizing costs for sustained usage. Ideal for ML engineers tackling trillion-parameter models or complex simulations, it offers unparalleled efficiency in FP8/FP4 precision, transformer engine advancements, and up to 30x inference gains over prior generations. While public benchmarks are emerging, early indicators highlight its dominance in agentic AI and multimodal training, backed by Cirrascale's focus on raw, uncompromised compute.
Why NVIDIA B200 SXM on Cirrascale?
Choose Cirrascale for NVIDIA B200 SXM due to its bare-metal dedication, eliminating virtualization latency and noisy neighbors for peak GPU utilization—critical for Blackwell's high-bandwidth demands. Monthly billing suits prolonged training runs, avoiding hourly waste on idle time. Cirrascale's infrastructure complements B200 with robust multi-GPU NVLink interconnects and high-speed networking, enabling seamless scaling for distributed workloads. The provider's diverse hardware ecosystem allows hybrid testing (e.g., NVIDIA with AMD/Qualcomm), while non-virtualized servers guarantee consistent performance for research reproducibility. This setup maximizes B200's efficiency in memory-intensive tasks like MoE models, offering cost-effective access to cutting-edge Blackwell without the lock-in of hyperscalers.
Live Pricing
Real-time NVIDIA B200 SXM offers from Cirrascale
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
Cirrascale | 8×NVIDIA B200 SXM 192GB VRAM | 192GB | 192 vCPU 2048GB RAM 43923GB Storage | United States | $4.79/GPU/hr $38.32/hr total (8×) | |||
Cirrascale | 8×NVIDIA B200 SXM 192GB VRAM | 192GB | 192 vCPU 2048GB RAM 43923GB Storage | United States | $5.39/GPU/hr $43.12/hr total (8×) | |||
Cirrascale | 8×NVIDIA B200 SXM 192GB VRAM | 192GB | 192 vCPU 2048GB RAM 43923GB Storage | United States | $5.69/GPU/hr $45.52/hr total (8×) | |||
Cirrascale | 8×NVIDIA B200 SXM 192GB VRAM | 192GB | 192 vCPU 2048GB RAM 43923GB Storage | United States | $5.99/GPU/hr $47.92/hr total (8×) |
Performance Notes
On Cirrascale, NVIDIA B200 SXM is expected to achieve full Blackwell specs: up to 20 petaFLOPS FP4 AI performance per GPU, with 192GB HBM3e at 8 TB/s bandwidth. Bare-metal deployment ensures optimal NVLink for 8-GPU scaling (up to 1.8 TB aggregate VRAM), low-latency inter-node via InfiniBand or Ethernet (exact speeds TBD). Storage likely includes high-IOPS NVMe for datasets, supporting DGX-like configs. Multi-GPU scaling excels in NCCL collectives for PyTorch/DeepSpeed. While vendor benchmarks show 4x training speedups over H100, real-world Cirrascale results are pending; anticipate excellent consistency but verify network fabric for >100Gbps clusters. No virtualization tax enhances reproducibility.
An AI Innovation Cloud targeting deep learning and HPC research with dedicated performance on non-virtualized hardware.
Best For
Unique Features
- Diverse hardware stack including Qualcomm, AMD, and NVIDIA accelerators
- Bare-metal dedicated servers
VRAM
192GB
Architecture
Blackwell
Tier
enterprise
Platform Features
Getting Started
Getting started with NVIDIA B200 SXM on Cirrascale is straightforward for experienced ML users. Sign up for a monthly bare-metal instance via their portal, select B200 configurations, deploy pre-imaged Ubuntu servers with NVIDIA drivers, and access via SSH/Jupyter. Tailored for research, it supports immediate CUDA workloads with minimal setup.
Steps
- 1Create a Cirrascale account and verify via email for access to the dashboard.
- 2Browse GPU catalog, select NVIDIA B200 SXM bare-metal server (e.g., 8x GPU node), and choose storage/network options.
- 3Commit to monthly billing and deploy; provisioning takes 5-30 minutes.
- 4SSH into the instance using provided keys; run 'nvidia-smi' to confirm B200 detection.
- 5Install frameworks like PyTorch via pip/conda and launch your training script.
Pro Tips
- Leverage NVLink for multi-GPU by using NCCL backend in PyTorch; test scaling with torch.distributed to ensure full interconnect utilization.
- For long jobs, enable auto-scaling storage and monitor via Prometheus/Grafana integrations to optimize resource allocation.
- Experiment with Cirrascale's diverse stack by starting on B200 then migrating to AMD for cost comparisons in inference benchmarks.
Frequently Asked Questions
What is Cirrascale's billing model for NVIDIA B200 SXM?▾
Cirrascale bills monthly for GPU instances including NVIDIA B200 SXM. Monthly billing is best suited for long-running, steady-state workloads where you need consistent access to GPU resources.
Does Cirrascale offer spot instances for NVIDIA B200 SXM?▾
No, Cirrascale does not currently offer spot instances for NVIDIA B200 SXM. All instances are billed at on-demand rates. However, they do offer reserved instances for committed usage, which can provide significant discounts for long-term workloads.
How can I access NVIDIA B200 SXM instances on Cirrascale?▾
Cirrascale provides access to NVIDIA B200 SXM instances via SSH. SSH access gives you full control over the instance for custom configurations and production deployments.
What compliance certifications does Cirrascale have for NVIDIA B200 SXM workloads?▾
Cirrascale does not have publicly listed compliance certifications. If your workloads require specific compliance standards (SOC 2, HIPAA, GDPR, etc.), contact them directly to discuss your requirements or consider a provider with the necessary certifications.
Can I use NVIDIA B200 SXM with Kubernetes on Cirrascale?▾
Yes, Cirrascale supports Kubernetes for orchestrating NVIDIA B200 SXM workloads. This enables you to deploy scalable ML pipelines, manage distributed training jobs across multiple GPUs, and integrate with MLOps tools like Kubeflow, Argo Workflows, and KServe. Kubernetes support is essential for teams building production-grade ML infrastructure.
What are the specifications of the NVIDIA B200 SXM?▾
The NVIDIA B200 SXM features 192GB of high-bandwidth memory, built on NVIDIA's Blackwell architecture. As an enterprise-tier GPU, it's designed for large-scale AI training, inference at scale, and demanding HPC workloads. The substantial VRAM capacity supports large language models, complex neural networks, and multi-model deployments.
What workloads is NVIDIA B200 SXM on Cirrascale best suited for?▾
The NVIDIA B200 SXM on Cirrascale is well-suited for large-scale AI/ML training, LLM fine-tuning, batch inference at scale, and high-performance computing workloads. Cirrascale specifically excels at: Research teams needing consistent, non-virtualized multi-GPU performance for long-training jobs. Consider your model size, training data volume, and latency requirements when evaluating this combination for your specific use case.
Does Cirrascale offer reserved instances for NVIDIA B200 SXM?▾
Yes, Cirrascale offers reserved instance pricing for NVIDIA B200 SXM, which can provide significant discounts (typically 20-40% off on-demand rates) for committed usage periods. Reserved instances are ideal for predictable, long-running workloads like production inference services, ongoing training pipelines, or development environments that run continuously. Contact Cirrascale for current reserved pricing and commitment terms.
What unique features does Cirrascale offer for NVIDIA B200 SXM?▾
Cirrascale differentiates itself with: Diverse hardware stack including Qualcomm, AMD, and NVIDIA accelerators; Bare-metal dedicated servers. These features may provide advantages depending on your specific workflow requirements and technical needs. Evaluate how these capabilities align with your ML infrastructure goals when making your decision.
How do I get started with NVIDIA B200 SXM on Cirrascale?▾
To get started with NVIDIA B200 SXM on Cirrascale, visit https://www.cirrascale.com?utm_source=gpuperhour&utm_medium=referral to create an account. Most providers offer a straightforward signup process, and some provide initial credits for new users. Once registered, you can typically launch a NVIDIA B200 SXM instance within minutes through their dashboard or API. We recommend starting with a small experiment to familiarize yourself with the platform before scaling up to larger workloads.
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