A100 PCIe 80GB on Massed Compute
Visit Massed ComputeMassed Compute offers the NVIDIA A100 PCIe 80GB GPU in high-performance virtual machines tailored for remote workstations and engineering simulations. This enterprise-tier Ampere GPU provides 80GB GDDR6 VRAM, enabling ML engineers to tackle memory-intensive workloads like training large language models, high-resolution image generation, and complex HPC simulations that overwhelm lesser GPUs. The combination stands out due to Massed Compute's ThinLinc technology, delivering low-latency, high-fidelity remote desktop access superior to standard VNC or RDP, ideal for distributed teams needing seamless graphical interaction. Per-hour billing offers flexibility for bursty or experimental workloads without long-term lock-in. Targeted at data scientists, AI researchers, and simulation engineers, key value propositions include boutique-grade reliability, ample VRAM for oversized batches/datasets, and optimized infrastructure minimizing remote access bottlenecks. While not the cheapest option, it excels where remote usability and GPU potency intersect.
Why NVIDIA A100 PCIe 80GB on Massed Compute?
Massed Compute pairs the A100 PCIe 80GB with strengths in remote workstation excellence, making it ideal for users needing high-fidelity GPU access without local hardware. ThinLinc technology provides buttery-smooth remote desktops, outperforming commoditized cloud RDP for visualization-heavy ML tasks like tensorboard or CAD sims. Boutique focus ensures low oversubscription, delivering consistent A100 performance for AI/HPC. Per-hour billing suits intermittent use, complementing the GPU's versatility in FP64-heavy simulations or transformer training. Unlike hyperscalers, this setup prioritizes single/multi-GPU VMs for engineering workflows, offering 80GB VRAM without SXM premiums. Choose this for superior remote ergonomics enhancing the A100's compute density.
Live Pricing
Real-time NVIDIA A100 PCIe 80GB offers from Massed Compute
No offers currently available for NVIDIA A100 PCIe 80GB on Massed Compute.
View NVIDIA A100 PCIe 80GB from all providersPerformance Notes
Expect flagship A100 PCIe 80GB performance on Massed Compute: ~19.5 TFLOPS FP64, 156 TFLOPS TF32, and 624 TFLOPS FP16 (with sparsity) for AI training/inference. 80GB GDDR6 handles massive models/datasets effectively. Network bandwidth likely 10-100Gbps, adequate for remote VMs but unspecified—suitable for single-node work, less optimal for massive distributed jobs. Fast NVMe storage options support quick data loading. Multi-GPU scaling via PCIe (no NVLink) limits bandwidth vs. SXM variants; provider supports configs for workstations, but cluster-scale unknown. ThinLinc adds negligible overhead. Limited public benchmarks; user feedback highlights responsive remote perf, though confirm via trial for your workload.
A boutique provider focusing on high-performance VMs for remote workstations and simulations.
Best For
Unique Features
- ThinLinc technology for superior remote desktop performance
VRAM
80GB
Architecture
Ampere
Tier
enterprise
Platform Features
Getting Started
Launching NVIDIA A100 PCIe 80GB on Massed Compute is user-friendly for ML pros. Sign up via their site, select from pre-configured high-perf VM templates optimized for AI/simulations, customize specs, and deploy. ThinLinc enables instant low-latency remote access; SSH/VNC alternatives available. Per-hour billing starts immediately upon launch, with easy scaling.
Steps
- 1Create a free account on the Massed Compute website and verify email.
- 2Navigate to VM dashboard, select A100 PCIe 80GB instance type.
- 3Configure CPU/RAM/storage/OS (e.g., Ubuntu with CUDA pre-installed).
- 4Launch instance; note IP and ThinLinc credentials provided.
- 5Connect via ThinLinc client or SSH, install ML frameworks like PyTorch.
Pro Tips
- Prioritize ThinLinc for graphical tasks—install client locally for sub-50ms latency on sims/visualizations.
- Pre-warm datasets on NVMe storage to maximize A100's memory bandwidth for training runs.
- Track usage dashboard for per-hour costs; snapshot instances to resume workflows cost-effectively.
Frequently Asked Questions
What is Massed Compute's billing model for NVIDIA A100 PCIe 80GB?▾
Massed Compute bills per-hour for GPU instances including NVIDIA A100 PCIe 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 Massed Compute offer spot instances for NVIDIA A100 PCIe 80GB?▾
No, Massed Compute does not currently offer spot instances for NVIDIA A100 PCIe 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.
How can I access NVIDIA A100 PCIe 80GB instances on Massed Compute?▾
Massed Compute provides access to NVIDIA A100 PCIe 80GB instances via SSH, built-in Jupyter notebooks, Docker containers. 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 Massed Compute have for NVIDIA A100 PCIe 80GB workloads?▾
Massed Compute 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 A100 PCIe 80GB with Kubernetes on Massed Compute?▾
Massed Compute does not prominently advertise native Kubernetes support. You may need to manage your own Kubernetes cluster or use alternative orchestration methods. However, they do support Docker containers, which can be a stepping stone to container orchestration.
What are the specifications of the NVIDIA A100 PCIe 80GB?▾
The NVIDIA A100 PCIe 80GB features 80GB 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 PCIe 80GB on Massed Compute best suited for?▾
The NVIDIA A100 PCIe 80GB on Massed Compute is well-suited for large-scale AI/ML training, LLM fine-tuning, batch inference at scale, and high-performance computing workloads. Massed Compute specifically excels at: Remote workstations; Engineering simulations. Consider your model size, training data volume, and latency requirements when evaluating this combination for your specific use case.
Does Massed Compute offer reserved instances for NVIDIA A100 PCIe 80GB?▾
Yes, Massed Compute offers reserved instance pricing for NVIDIA A100 PCIe 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 Massed Compute for current reserved pricing and commitment terms.
What unique features does Massed Compute offer for NVIDIA A100 PCIe 80GB?▾
Massed Compute differentiates itself with: ThinLinc technology for superior remote desktop performance. 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 PCIe 80GB on Massed Compute?▾
To get started with NVIDIA A100 PCIe 80GB on Massed Compute, visit https://massedcompute.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 PCIe 80GB 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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