A100 SXM4 40GB on VERDA
Visit VERDAVERDA provides the NVIDIA A100 SXM4 40GB, an enterprise-grade Ampere architecture GPU with 40GB HBM2e VRAM, optimized for AI training, inference, data analytics, and HPC workloads. Delivering up to 312 TFLOPS in TF32 tensor performance and 19.5 TFLOPS FP64, it excels in large-scale deep learning models like transformers. VERDA's green computing focus sets this offering apart: by capturing GPU waste heat for district heating, it reduces carbon emissions, appealing to sustainability-conscious organizations in Europe. Ideal for ML engineers and data scientists running resource-intensive workloads with ESG mandates, this per-hour billed instance combines top-tier compute with environmental responsibility. Low-latency European data centers ensure compliance with data sovereignty regs, while flexible scaling supports bursty AI pipelines. Though provider-specific benchmarks are sparse, expect reliable enterprise performance for multi-GPU training setups.
Why NVIDIA A100 SXM4 40GB on VERDA?
Choose VERDA for NVIDIA A100 SXM4 40GB if sustainability is paramount alongside high performance. VERDA's innovative waste heat recovery for district heating slashes the carbon footprint of AI compute—unique among GPU clouds—aligning with EU green initiatives. Europe-based infrastructure offers low latency for regional users, ideal for GDPR-compliant workloads. Per-hour billing provides cost flexibility for variable training jobs, avoiding long-term commitments. This complements the A100's efficiency in memory-bound tasks like fine-tuning LLMs, enabling eco-friendly scaling without performance trade-offs. For teams balancing ESG reporting with deadlines, it's a strategic pick over generic providers.
Live Pricing
Real-time NVIDIA A100 SXM4 40GB offers from VERDA
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
VERDA | NVIDIA A100 SXM4 40GB 40GB VRAM | 40GB | 22 vCPU 120GB RAM | Finland | $0.72/GPU/hr | Sold Out | ||
VERDA | NVIDIA A100 SXM4 40GB 40GB VRAM | 40GB | 22 vCPU 120GB RAM | Finland | $0.72/GPU/hr | Sold Out | ||
VERDA | NVIDIA A100 SXM4 40GB 40GB VRAM | 40GB | 22 vCPU 120GB RAM | Finland | $0.72/GPU/hr | Sold Out | ||
VERDA | 8×NVIDIA A100 SXM4 40GB 40GB VRAM | 40GB | 176 vCPU 960GB RAM | Finland | $0.72/GPU/hr $5.77/hr total (8×) | Sold Out | ||
VERDA | 8×NVIDIA A100 SXM4 40GB 40GB VRAM | 40GB | 176 vCPU 960GB RAM | Helsinki | $0.72/GPU/hr $5.77/hr total (8×) | Sold Out |
Performance Notes
The A100 SXM4 40GB delivers standard Ampere benchmarks: 312 TFLOPS TF32, 624 TFLOPS with sparsity, and robust MIG for workload isolation. VERDA's data center setup likely supports NVLink for multi-GPU (up to 8x scaling efficiency in NCCL tests), but interconnect details like InfiniBand speeds (100-400Gbps?) are not publicly detailed—check docs or support. Storage options and NVMe throughput unknown; expect SSDs suitable for checkpoints. Single-node perf matches NVIDIA refs; multi-node scaling depends on provider fabric. Benchmarks limited; validate with small runs for your workload.
A provider focused on green computing using waste heat for district heating.
Best For
Unique Features
- Use of waste heat for district heating
- Green computing focus
VRAM
40GB
Architecture
Ampere
Tier
enterprise
Platform Features
Getting Started
Getting started with VERDA's A100 SXM4 40GB is straightforward for ML users. Sign up, select the GPU instance, launch via dashboard, and connect securely. Pre-installed CUDA and frameworks like PyTorch speed up workflows, with per-hour billing for quick experimentation.
Steps
- 1Create a VERDA account and verify via email.
- 2Navigate to the GPU instance marketplace and select A100 SXM4 40GB.
- 3Configure CPU/RAM/storage, review hourly pricing, and launch instance.
- 4Connect via SSH (with key pair) or JupyterLab web interface.
- 5Install dependencies (e.g., pip install torch) and run your workload.
Pro Tips
- Track usage hourly to optimize costs; shut down idle instances promptly.
- Use MIG partitioning for concurrent workloads on the 40GB VRAM.
- Leverage VERDA's sustainability metrics for ESG reports if available.
Frequently Asked Questions
What is VERDA's billing model for NVIDIA A100 SXM4 40GB?▾
VERDA bills per-hour for GPU instances including NVIDIA A100 SXM4 40GB. Hourly billing means you pay for full hours even if your job completes mid-hour. Plan your workloads accordingly to maximize cost efficiency.
Does VERDA offer spot instances for NVIDIA A100 SXM4 40GB?▾
No, VERDA does not currently offer spot instances for NVIDIA A100 SXM4 40GB. 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 A100 SXM4 40GB instances on VERDA?▾
VERDA provides access to NVIDIA A100 SXM4 40GB instances via SSH, built-in Jupyter notebooks, programmatic API. 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. API access enables automation and integration with your existing ML pipelines and CI/CD workflows.
What compliance certifications does VERDA have for NVIDIA A100 SXM4 40GB workloads?▾
VERDA maintains GDPR, ISO 27001 certifications, making it suitable for regulated workloads. Contact VERDA directly for detailed compliance documentation and BAA agreements if needed.
Can I use NVIDIA A100 SXM4 40GB with Kubernetes on VERDA?▾
Yes, VERDA supports Kubernetes for orchestrating NVIDIA A100 SXM4 40GB 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 A100 SXM4 40GB?▾
The NVIDIA A100 SXM4 40GB features 40GB of high-bandwidth memory, built on NVIDIA's Ampere 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 A100 SXM4 40GB on VERDA best suited for?▾
The NVIDIA A100 SXM4 40GB on VERDA is well-suited for large-scale AI/ML training, LLM fine-tuning, batch inference at scale, and high-performance computing workloads. VERDA specifically excels at: Sustainable AI training in Europe. Consider your model size, training data volume, and latency requirements when evaluating this combination for your specific use case.
Does VERDA offer reserved instances for NVIDIA A100 SXM4 40GB?▾
Yes, VERDA offers reserved instance pricing for NVIDIA A100 SXM4 40GB, 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 VERDA for current reserved pricing and commitment terms.
What unique features does VERDA offer for NVIDIA A100 SXM4 40GB?▾
VERDA differentiates itself with: Use of waste heat for district heating; Green computing focus. 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 A100 SXM4 40GB on VERDA?▾
To get started with NVIDIA A100 SXM4 40GB on VERDA, visit https://verda.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 A100 SXM4 40GB 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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