A100 SXM4 80GB on VERDA
Visit VERDAVERDA's NVIDIA A100 SXM4 80GB offering combines a premier enterprise GPU with pioneering green computing infrastructure, making it a standout choice for sustainable AI workloads in Europe. The A100 SXM4, built on Ampere architecture, delivers 80GB HBM2e VRAM, up to 312 TFLOPS in FP16 (with sparsity), and excels in training large-scale models, data analytics, and HPC tasks. VERDA repurposes data center waste heat for district heating, drastically reducing the environmental footprint of power-hungry GPUs like the A100. Targeted at ML engineers and data scientists prioritizing ESG compliance, this setup supports memory-intensive applications such as LLMs and multimodal AI without on-prem hardware investments. Key value propositions include flexible per-hour billing for cost-effective experimentation, European data residency for low-latency compliance (GDPR), and enterprise reliability. While performance rivals bare-metal DGX systems, VERDA's eco-focus appeals to sustainability-driven teams, enabling high-throughput training with a conscience. Ideal for organizations balancing computational demands with green mandates.
Why NVIDIA A100 SXM4 80GB on VERDA?
Opt for VERDA's A100 SXM4 80GB when sustainability intersects with elite performance. VERDA's waste heat recovery for district heating offsets the GPU's ~400W TDP, achieving carbon-neutral heating contributions—unique among GPU clouds. Europe-based data centers ensure low-latency for regional users and regulatory alignment. Per-hour billing enables pay-as-you-go for iterative ML workflows, complementing the 80GB VRAM for handling massive datasets or long-context models. This pairing leverages the A100's NVLink scalability in eco-optimized clusters, reducing total cost of ownership via energy efficiency. For green AI pioneers, it provides verifiable impact metrics, distinguishing it from generic providers while delivering uncompromised Ampere power.
Live Pricing
Real-time NVIDIA A100 SXM4 80GB offers from VERDA
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 VERDA | 4×NVIDIA A100 SXM4 80GB 80GB VRAM | 80GB | 88 vCPU 480GB RAM | Finland | $1.29/GPU/hr $5.16/hr total (4×) | Sold Out | ||
VERDA | 8×NVIDIA A100 SXM4 80GB 80GB VRAM | 80GB | 176 vCPU 960GB RAM | Finland | $1.29/GPU/hr $10.32/hr total (8×) | Sold Out | ||
VERDA | NVIDIA A100 SXM4 80GB 80GB VRAM | 80GB | 22 vCPU 120GB RAM | Helsinki | $1.29/GPU/hr | Sold Out | ||
VERDA | 8×NVIDIA A100 SXM4 80GB 80GB VRAM | 80GB | 176 vCPU 960GB RAM | Finland | $1.29/GPU/hr $10.32/hr total (8×) | Sold Out | ||
VERDA | 8×NVIDIA A100 SXM4 80GB 80GB VRAM | 80GB | 176 vCPU 960GB RAM | Helsinki | $1.29/GPU/hr $10.32/hr total (8×) | Sold Out |
Performance Notes
Expect standard A100 SXM4 80GB benchmarks on VERDA: 19.5 TFLOPS FP64, 624 TFLOPS Tensor FP16, with 2,000GB/s HBM2e bandwidth. Multi-GPU scaling via NVLink 3.0 (600GB/s/node) supports efficient distributed training; InfiniBand or high-speed Ethernet (details unconfirmed) aids cluster performance. Storage likely includes fast NVMe SSDs for datasets, but specifics are limited—assume 10-100Gbps networking suitable for ML. VERDA's advanced cooling from waste heat reuse may sustain peak clocks longer than air-cooled peers. Real-world benchmarks scarce; test for your workload. No known throttling issues, but monitor via NVIDIA DCGM for optimal results.
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
80GB
Architecture
Ampere
Tier
enterprise
Platform Features
Getting Started
Launch VERDA's A100 SXM4 80GB quickly via their intuitive dashboard. Accounts activate rapidly with per-hour billing. Pre-configured images include CUDA 12+, PyTorch/TensorFlow, enabling instant ML prototyping. Connect securely via SSH/Jupyter; scale to multi-GPU as needed for sustainable, high-VRAM training.
Steps
- 1Sign up on VERDA's website, verify email, and add a payment method for instant access.
- 2Log in to the dashboard, search for NVIDIA A100 SXM4 80GB, and select instance size.
- 3Configure vCPUs, RAM, storage volume, and OS image; review hourly rate and deploy.
- 4Retrieve connection details (SSH key, IP), then access the instance securely.
- 5Pull Docker containers or install ML frameworks to start training workloads.
Pro Tips
- Use NVIDIA NGC containers for A100-optimized CUDA, frameworks, and models to accelerate setup and performance.
- Track sustainability metrics in VERDA's dashboard to report waste heat reuse for ESG compliance.
- Request NVLink clusters early for multi-GPU jobs; test scaling with NCCL benchmarks for efficiency.
Frequently Asked Questions
What is VERDA's billing model for NVIDIA A100 SXM4 80GB?▾
VERDA bills per-hour for GPU instances including NVIDIA A100 SXM4 80GB. 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 80GB?▾
No, VERDA does not currently offer spot instances for NVIDIA A100 SXM4 80GB. 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.
Does VERDA offer reserved instances for NVIDIA A100 SXM4 80GB?▾
Yes, VERDA offers reserved instance pricing for NVIDIA A100 SXM4 80GB, 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.