Quadro RTX 4000 on Paperspace
Visit PaperspacePaperspace's NVIDIA Quadro RTX 4000 offering combines a professional workstation GPU with the Gradient MLOps platform, ideal for individual developers and educational users tackling ML workflows. Featuring 8GB GDDR6 VRAM on the Turing architecture, this GPU delivers 2304 CUDA cores, 288 Tensor cores, and RT cores for ray-traced rendering, balancing compute for training small-to-medium models, inference, and visualization tasks like 3D modeling or CAD in ML pipelines. Paperspace's per-second billing minimizes costs for sporadic use, while Gradient streamlines notebook-to-deployment with Jupyter integration, versioning, and team collaboration. This setup shines for prototyping, fine-tuning lighter models (e.g., BERT-base, Stable Diffusion inference), and educational experiments where high-end datacenter GPUs are overkill. Key value propositions include instant spin-up, no long-term commitments, and seamless scaling within Gradient's ecosystem, making it accessible for ML engineers evaluating workstation-grade performance without infrastructure overhead. Limitations include its 8GB VRAM cap, suiting it less for large-batch training compared to modern A-series GPUs.
Why NVIDIA Quadro RTX 4000 on Paperspace?
Choose Paperspace for the Quadro RTX 4000 if you need a cost-effective workstation GPU optimized for ML prototyping and professional visualization within an intuitive MLOps platform. Paperspace's Gradient excels at simplifying workflows from Jupyter notebooks to deployments, complementing the RTX 4000's strengths in FP32 compute (7.1 TFLOPS), TensorRT acceleration, and real-time ray tracing for ML-driven graphics tasks. Per-second billing (starting ~$0.45/hour) enables low-risk experimentation, ideal for individuals or students. Unlike raw cloud VMs, Gradient offers built-in experiment tracking, model registry, and deployments, enhancing the GPU's versatility. Fast instance provisioning and persistent storage options reduce setup friction, making this combo superior for education, freelance ML work, or hybrid viz/ML projects over pricier multi-GPU setups.
Live Pricing
Real-time NVIDIA Quadro RTX 4000 offers from Paperspace
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
![]() Paperspace | 2×NVIDIA Quadro RTX 4000 8GB VRAM | 8GB | 16 vCPU 60GB RAM 50GB Storage | Amsterdam | $0.56/GPU/hr $1.12/hr total (2×) | Sold Out | ||
![]() Paperspace | NVIDIA Quadro RTX 4000 8GB VRAM | 8GB | 8 vCPU 30GB RAM 50GB Storage | New York | $0.56/GPU/hr | Available | ||
![]() Paperspace | NVIDIA Quadro RTX 4000 8GB VRAM | 8GB | 8 vCPU 30GB RAM 50GB Storage | Canada | $0.56/GPU/hr | Available | ||
![]() Paperspace | 2×NVIDIA Quadro RTX 4000 8GB VRAM | 8GB | 16 vCPU 60GB RAM 50GB Storage | New York | $0.56/GPU/hr $1.12/hr total (2×) | Available | ||
![]() Paperspace | NVIDIA Quadro RTX 4000 8GB VRAM | 8GB | 8 vCPU 30GB RAM 50GB Storage | Amsterdam | $0.56/GPU/hr | Available |





Performance Notes
On Paperspace, the Quadro RTX 4000 delivers solid Turing-era performance: ~7.1 TFLOPS FP32, 57 TFLOPS Tensor FP16, suitable for training models up to ~7GB (e.g., ResNet-50, lightweight transformers) or high-res inference. Expect single-GPU limits—no NVLink scaling noted. Network bandwidth reaches 1-10 Gbps (provider-standard), adequate for dataset pulls but not massive distributed training. Storage includes fast NVMe SSDs (up to 1TB burst), with Gradient's persistent volumes for checkpoints. Real-world ML benchmarks show 2-3x slower than RTX 3090 for training, but excels in viz-heavy tasks. Performance is consistent across regions; monitor via nvidia-smi. Unknowns: exact P3 instance variability—test for your workload.
A provider offering the Gradient MLOps platform for simplifying notebook-to-deployment workflows.
Best For
Unique Features
- Gradient platform for ML workflows
VRAM
8GB
Architecture
Turing
Tier
workstation
Platform Features
Getting Started
Launching NVIDIA Quadro RTX 4000 on Paperspace is straightforward via the Gradient console. Sign up for a free account, select the GPU machine type, and deploy in minutes. Ideal for quick ML prototyping with pre-installed CUDA, cuDNN, and JupyterLab support.
Steps
- 1Sign up or log in to Paperspace Console and navigate to Gradient > Machines.
- 2Click 'New Machine', select 'Quadro RTX 4000' under GPU options, choose region/OS.
- 3Configure storage (e.g., 50GB SSD), set per-second billing, and launch.
- 4Connect via JupyterLab or SSH; verify GPU with 'nvidia-smi' command.
- 5Start ML workflows—install libs via pip/conda in the notebook interface.
Pro Tips
- Use Gradient's Experiment Tracking for versioning runs automatically, saving time on repro and comparisons.
- Enable persistent storage early to retain datasets/models across sessions, avoiding re-downloads.
- For viz tasks, leverage RTX 4000's RT cores with NVIDIA Omniverse or Paraview integrations in Jupyter.
Frequently Asked Questions
What is Paperspace's billing model for NVIDIA Quadro RTX 4000?▾
Paperspace bills per-second for GPU instances including NVIDIA Quadro RTX 4000. 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 RTX 4000?▾
No, Paperspace does not currently offer spot instances for NVIDIA Quadro RTX 4000. 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 RTX 4000 instances on Paperspace?▾
Paperspace provides access to NVIDIA Quadro RTX 4000 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 RTX 4000 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 RTX 4000 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 RTX 4000?▾
The NVIDIA Quadro RTX 4000 features 8GB of high-bandwidth memory, built on NVIDIA's Turing 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 RTX 4000 on Paperspace best suited for?▾
The NVIDIA Quadro RTX 4000 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 RTX 4000?▾
Yes, Paperspace offers reserved instance pricing for NVIDIA Quadro RTX 4000, 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 RTX 4000?▾
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 RTX 4000 on Paperspace?▾
To get started with NVIDIA Quadro RTX 4000 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 RTX 4000 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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