Quadro P5000 on Paperspace
Visit PaperspacePaperspace's NVIDIA Quadro P5000 offering combines a high-end Pascal workstation GPU with 16GB GDDR5X VRAM and the Gradient MLOps platform, making it noteworthy for accessible ML prototyping and visualization. Targeted at individual developers and educational users, it excels in simplifying notebook-to-deployment workflows via Gradient's collaborative notebooks, versioning, and deployment tools. Key value propositions include per-second billing for cost-effective, on-demand access; instant instance spin-up; and seamless integration with popular ML frameworks like TensorFlow and PyTorch. The P5000's 2560 CUDA cores, 9.3 TFLOPS FP32 performance, and high memory bandwidth suit mid-sized model training, inference, and professional viz tasks. While Pascal architecture lags behind newer Ampere/Ada options for cutting-edge training, its 16GB VRAM handles many practical ML workloads efficiently on Paperspace's reliable infrastructure, offering a balanced entry point without overprovisioning.
Why NVIDIA Quadro P5000 on Paperspace?
Paperspace pairs the Quadro P5000 ideally with its Gradient platform, enabling seamless ML workflows from interactive notebooks to production deployments—perfect for solo devs and educators prototyping visualization-heavy or memory-intensive tasks. Per-second billing optimizes costs for bursty workloads, complementing the P5000's workstation strengths in CAD/DCC alongside ML inference. Fast provisioning, persistent storage, and Jupyter integration reduce setup friction, while Paperspace's focus on simplicity enhances the GPU's 16GB VRAM utility for mid-tier models without enterprise overhead. This combo stands out for quick experimentation over raw compute power.
Live Pricing
Real-time NVIDIA Quadro P5000 offers from Paperspace
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
![]() Paperspace | 2×NVIDIA Quadro P5000 16GB VRAM | 16GB | 16 vCPU 60GB RAM 50GB Storage | Amsterdam | $0.78/GPU/hr $1.56/hr total (2×) | Available | ||
![]() Paperspace | 2×NVIDIA Quadro P5000 16GB VRAM | 16GB | 16 vCPU 60GB RAM 50GB Storage | Canada | $0.78/GPU/hr $1.56/hr total (2×) | Available | ||
![]() Paperspace | 2×NVIDIA Quadro P5000 16GB VRAM | 16GB | 16 vCPU 60GB RAM 50GB Storage | New York | $0.78/GPU/hr $1.56/hr total (2×) | Available | ||
![]() Paperspace | NVIDIA Quadro P5000 16GB VRAM | 16GB | 8 vCPU 30GB RAM 50GB Storage | Amsterdam | $0.78/GPU/hr | Available | ||
![]() Paperspace | NVIDIA Quadro P5000 16GB VRAM | 16GB | 8 vCPU 30GB RAM 50GB Storage | New York | $0.78/GPU/hr | Available |





Performance Notes
The Quadro P5000 delivers ~9.3 TFLOPS FP32 and ~192 GB/s memory bandwidth on Paperspace, performing well for prototyping, fine-tuning <10B param models, and inference. Expect standard 1Gbps networking (upgradable), NVMe storage options up to 2TB, and low-latency Jupyter access via Gradient. Single-GPU only; no native multi-GPU scaling for this tier. Performance mirrors on-prem benchmarks with minimal cloud overhead, but Pascal limits tensor core efficiency vs. modern GPUs. Network/storage speeds are provider-standard; exact P5000 interconnect details undocumented—test for workload-specific latency.
A provider offering the Gradient MLOps platform for simplifying notebook-to-deployment workflows.
Best For
Unique Features
- Gradient platform for ML workflows
VRAM
16GB
Architecture
Pascal
Tier
workstation
Platform Features
Getting Started
Launching NVIDIA Quadro P5000 on Paperspace is quick via the web console or Gradient. Ideal for ML engineers, it supports instant GPU access with pre-configured images, per-second billing, and easy scaling for prototyping workflows.
Steps
- 1Sign up for a Paperspace account at paperspace.com/gradient.
- 2In Console or Gradient, select 'Create Machine' and choose Quadro P5000.
- 3Pick region, CPU/RAM (e.g., 8 vCPU/32GB), storage, and ML image like PyTorch.
- 4Launch instance; connect via SSH, web Jupyter, or Gradient notebook.
- 5Run benchmarks or workloads; stop instance to pause billing.
Pro Tips
- Leverage Gradient for collaborative notebooks and auto-deployments to streamline ML pipelines beyond basic compute.
- Monitor real-time costs in the console and use public IP for external data access to optimize per-second billing.
- Pre-load datasets to persistent volumes for faster iteration on memory-bound P5000 workloads.
Frequently Asked Questions
What is Paperspace's billing model for NVIDIA Quadro P5000?▾
Paperspace bills per-second for GPU instances including NVIDIA Quadro P5000. Per-second billing ensures you only pay for exactly the compute time you use, which is particularly cost-effective for short experiments, iterative development, and workloads with variable duration.
Does Paperspace offer spot instances for NVIDIA Quadro P5000?▾
No, Paperspace does not currently offer spot instances for NVIDIA Quadro P5000. 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 Quadro P5000 instances on Paperspace?▾
Paperspace provides access to NVIDIA Quadro P5000 instances via SSH, built-in Jupyter notebooks, web-based terminal, 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 Paperspace have for NVIDIA Quadro P5000 workloads?▾
Paperspace maintains SOC 2, GDPR certifications, making it suitable for regulated workloads. SOC 2 certification demonstrates strong security controls for handling sensitive data. Contact Paperspace directly for detailed compliance documentation and BAA agreements if needed.
Can I use NVIDIA Quadro P5000 with Kubernetes on Paperspace?▾
Paperspace 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 Quadro P5000?▾
The NVIDIA Quadro P5000 features 16GB of high-bandwidth memory, built on NVIDIA's Pascal architecture. As a workstation-class GPU, it's well-suited for professional visualization, rendering, and medium-scale ML tasks. It offers a good balance of performance and cost for development and smaller production workloads.
What workloads is NVIDIA Quadro P5000 on Paperspace best suited for?▾
The NVIDIA Quadro P5000 on Paperspace is well-suited for model development, fine-tuning, medium-scale training, and inference workloads. Paperspace specifically excels at: Individual developers and education. Consider your model size, training data volume, and latency requirements when evaluating this combination for your specific use case.
Does Paperspace offer reserved instances for NVIDIA Quadro P5000?▾
Yes, Paperspace offers reserved instance pricing for NVIDIA Quadro P5000, 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 Paperspace for current reserved pricing and commitment terms.
What unique features does Paperspace offer for NVIDIA Quadro P5000?▾
Paperspace differentiates itself with: Gradient platform for ML workflows. 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 Quadro P5000 on Paperspace?▾
To get started with NVIDIA Quadro P5000 on Paperspace, visit https://www.paperspace.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 Quadro P5000 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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