B300 SXM6 on Scaleway
Visit ScalewayScaleway's NVIDIA B300 SXM6 offering brings the cutting-edge Blackwell architecture to European cloud users, featuring 262GB of HBM3e VRAM for handling massive AI models and datasets. As a major European provider, Scaleway emphasizes data sovereignty with instances hosted in France and the Netherlands, ideal for regulated industries like finance and healthcare. This enterprise-tier GPU excels in large-scale AI training, inference, and HPC workloads, delivering unprecedented performance through its 20 petaFLOPS FP8 tensor compute. Key value propositions include flexible per-hour billing for cost efficiency, integration with Scaleway's Nabu AI Supercomputer for multi-GPU scaling, and strong environmental credentials via renewable energy-powered data centers. ML engineers evaluating options will appreciate the combination of raw power, compliance with GDPR and EU regulations, and seamless integration with Object Storage, Kubernetes, and serverless services, making it a top choice for sovereignty-focused AI deployments without compromising on performance.
Why NVIDIA B300 SXM6 on Scaleway?
Choosing Scaleway for the NVIDIA B300 SXM6 leverages Europe's leading data sovereignty provider with Blackwell's immense 262GB VRAM tailored for trillion-parameter models. Scaleway's Nabu AI Supercomputer enables seamless multi-GPU clusters up to thousands of GPUs, perfect for distributed training. Per-hour billing offers flexibility for bursty workloads, reducing costs compared to commitment-based models. High-speed NVMe storage and up to 400Gbps networking complement the GPU's capabilities, while integrated services like RKE2 Kubernetes and Object Storage streamline workflows. Strong environmental focus with 100% renewable energy appeals to sustainable AI teams. For EU-based organizations, this combo ensures compliance without latency penalties from transatlantic data transfers.
Live Pricing
Real-time NVIDIA B300 SXM6 offers from Scaleway
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
Scaleway | 8×NVIDIA B300 SXM6 262GB VRAM | 262GB | 224 vCPU 3840GB RAM 22352GB Storage | Paris | $8.73/GPU/hr $69.84/hr total (8×) | Available |
Performance Notes
The NVIDIA B300 SXM6 on Scaleway delivers Blackwell's transformative performance, with up to 20 petaFLOPS in FP8 for AI training and inference on models exceeding 1 trillion parameters, thanks to 262GB HBM3e VRAM. Expect excellent multi-GPU scaling via Nabu clusters using NVLink or InfiniBand (up to 400Gbps confirmed in docs). NVMe SSD storage options provide high IOPS for data loading. Network bandwidth supports efficient all-reduce operations in distributed setups. Actual benchmarks are emerging post-launch; early reports suggest 2-3x gains over Hopper GPUs. Limitations: cooling and power density may cap dense packing—verify Scaleway's instance configs. Monitor provider updates for optimized drivers and CUDA 12.x support.
A major European cloud provider emphasizing data sovereignty and integrated services.
Best For
Unique Features
- Nabu AI Supercomputer
- Strong environmental credentials
VRAM
262GB
Architecture
Blackwell
Tier
enterprise
Platform Features
Getting Started
Scaleway simplifies launching NVIDIA B300 SXM6 instances via their intuitive console or API, with per-hour billing starting on-demand. New users benefit from a free trial credit and pre-configured AI images with NVIDIA drivers, CUDA, and frameworks like PyTorch. Focus on EU regions for low-latency sovereignty.
Steps
- 1Create a Scaleway account and Organization at console.scaleway.com.
- 2Set up billing and select Paris or Amsterdam region for sovereignty.
- 3Navigate to Instances > GPU, choose B300 SXM6, configure vCPU/RAM/storage.
- 4Select AI-optimized OS image (e.g., Ubuntu with CUDA), launch instance.
- 5Connect via SSH or Jupyter, install workloads using NGC containers.
Pro Tips
- Use Scaleway's Elastic Metal for bare-metal B300 access if needing maximum performance without virtualization overhead.
- Leverage Nabu House clusters for multi-GPU jobs; pre-warm instances to minimize cold-start times for iterative training.
- Integrate with Scaleway Object Storage for datasets—enable snapshotting for quick recovery in long-running experiments.
Frequently Asked Questions
What is Scaleway's billing model for NVIDIA B300 SXM6?▾
Scaleway bills per-hour for GPU instances including NVIDIA B300 SXM6. Hourly billing means you pay for full hours even if your job completes mid-hour. Plan your workloads accordingly to maximize cost efficiency.
Does Scaleway offer spot instances for NVIDIA B300 SXM6?▾
No, Scaleway does not currently offer spot instances for NVIDIA B300 SXM6. 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 B300 SXM6 instances on Scaleway?▾
Scaleway provides access to NVIDIA B300 SXM6 instances via SSH, built-in Jupyter notebooks, web-based terminal. The built-in Jupyter notebook support makes it easy to start experimenting immediately without additional setup. SSH access gives you full control over the instance for custom configurations and production deployments.
What compliance certifications does Scaleway have for NVIDIA B300 SXM6 workloads?▾
Scaleway maintains SOC 2, GDPR, ISO 27001 certifications, making it suitable for regulated workloads. SOC 2 certification demonstrates strong security controls for handling sensitive data. Contact Scaleway directly for detailed compliance documentation and BAA agreements if needed.
Can I use NVIDIA B300 SXM6 with Kubernetes on Scaleway?▾
Yes, Scaleway supports Kubernetes for orchestrating NVIDIA B300 SXM6 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 B300 SXM6?▾
The NVIDIA B300 SXM6 features 262GB 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 B300 SXM6 on Scaleway best suited for?▾
The NVIDIA B300 SXM6 on Scaleway is well-suited for large-scale AI/ML training, LLM fine-tuning, batch inference at scale, and high-performance computing workloads. Scaleway specifically excels at: European data sovereignty; Integrated cloud services. Consider your model size, training data volume, and latency requirements when evaluating this combination for your specific use case.
Does Scaleway offer reserved instances for NVIDIA B300 SXM6?▾
Yes, Scaleway offers reserved instance pricing for NVIDIA B300 SXM6, 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 Scaleway for current reserved pricing and commitment terms.
What unique features does Scaleway offer for NVIDIA B300 SXM6?▾
Scaleway differentiates itself with: Nabu AI Supercomputer; Strong environmental credentials. 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 B300 SXM6 on Scaleway?▾
To get started with NVIDIA B300 SXM6 on Scaleway, visit https://www.scaleway.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 B300 SXM6 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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