RTX A4000 on TensorDock
Visit TensorDockTensorDock provides access to the NVIDIA RTX A4000, a 16GB Ampere architecture workstation GPU, through its innovative GPU marketplace model. This combination stands out for delivering extremely low spot prices on a professional-grade GPU optimized for visual computing and AI workloads. Stabilized by recent acquisition from Voltage Park, TensorDock ensures reliable inventory availability, minimizing disruptions common in spot markets. Ideal for ML engineers and data scientists handling smaller-scale model training, fine-tuning, inference, or visualization tasks where cost efficiency trumps raw datacenter-scale performance. Key value propositions include per-second billing for precise cost control, spot instances offering up to 70-80% savings over on-demand, and a diverse marketplace aggregating supply from multiple hosts. The RTX A4000's 6,144 CUDA cores, 192 Tensor cores, and features like RT cores deliver balanced FP32/FP16 performance (19.2/38.7 TFLOPS) with power efficiency under 140W, making it perfect for bursty, budget-conscious workflows without sacrificing professional reliability.
Why NVIDIA RTX A4000 on TensorDock?
Opt for TensorDock's RTX A4000 when prioritizing the lowest spot prices on a capable workstation GPU. The marketplace model aggregates global supply, driving down costs while Voltage Park's acquisition stabilizes inventory for consistent availability. This complements the A4000's strengths in efficient, single-GPU ML tasks like prototyping, inference on 16GB models, or visual AI pipelines. Per-second billing eliminates waste on short jobs, and spot instances enable aggressive savings (often sub-$0.20/hour). Unlike rigid datacenter providers, TensorDock's flexibility suits variable workloads, with quick provisioning and diverse host options enhancing the A4000's plug-and-play workstation heritage for rapid iteration.
Live Pricing
Real-time NVIDIA RTX A4000 offers from TensorDock
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 TensorDock | NVIDIA RTX A4000 16GB VRAM | 16GB | 0 vCPU 0GB RAM | Detroit, Michigan | $0.08/GPU/hr | Sold Out | ||
TensorDock | NVIDIA RTX A4000 16GB VRAM | 16GB | 0 vCPU 0GB RAM | Tallinn, Harjumaa | $0.09/GPU/hr | Sold Out | ||
TensorDock | NVIDIA RTX A4000 16GB VRAM | 16GB | 0 vCPU 0GB RAM | Tallinn, Harjumaa | $0.09/GPU/hr | Sold Out | ||
TensorDock | NVIDIA RTX A4000 16GB VRAM | 16GB | 0 vCPU 0GB RAM | Rzeszow, Subcarpathian | $0.10/GPU/hr | Sold Out | ||
TensorDock | NVIDIA RTX A4000 16GB VRAM | 16GB | 0 vCPU 0GB RAM | Raleigh, North Carolina | $0.11/GPU/hr | Sold Out |
Performance Notes
On TensorDock, the RTX A4000 delivers solid Ampere performance: ~19.2 TFLOPS FP32, ~38.7 TFLOPS FP16 with sparsity, suiting fine-tuning of mid-sized LLMs or vision models within 16GB VRAM. Expect host-dependent factors—network bandwidth typically 10-25Gbps, NVMe storage 1-4TB, but varies by marketplace host. Multi-GPU scaling possible in 2-4x configs on select instances, though PCIe interconnects limit to NVLink-free bandwidth (~32-64GB/s). Pre-installed NVIDIA drivers and CUDA support ML frameworks out-of-box. Known limitations: not datacenter-optimized like A100/H100, so lower inter-GPU efficiency; spot interruptions possible. Specific TensorDock benchmarks sparse—test for your workload as host variability impacts consistency.
A GPU marketplace offering extremely low spot prices, stabilized by acquisition by Voltage Park.
Best For
Unique Features
- Marketplace model
- Stabilized inventory post-acquisition
VRAM
16GB
Architecture
Ampere
Tier
workstation
Platform Features
Getting Started
Getting started with TensorDock's RTX A4000 is straightforward via their intuitive dashboard. Create an account, fund with credits, and launch spot or on-demand instances in minutes. Choose pre-configured ML images with NVIDIA drivers, CUDA, and Docker for seamless workflows. Connect via SSH or web console to deploy models instantly.
Steps
- 1Sign up at tendordock.com and verify your email.
- 2Deposit credits via card, wire, or crypto for billing.
- 3Browse marketplace, filter for RTX A4000, select spot/on-demand.
- 4Choose instance size, OS/image (e.g., Ubuntu CUDA), and deploy.
- 5Access via SSH (keys auto-generated) or VNC for GPU-accelerated tasks.
Pro Tips
- Bid aggressively on spot instances for 70%+ savings, but enable auto-relaunch scripts to handle interruptions.
- Use official NVIDIA NGC containers for PyTorch/TensorFlow to maximize A4000's Ampere features like TF32.
- Monitor VRAM usage with nvidia-smi; optimize batch sizes for 16GB limit in training/inference pipelines.
Frequently Asked Questions
What is TensorDock's billing model for NVIDIA RTX A4000?▾
TensorDock bills per-second for GPU instances including NVIDIA RTX A4000. 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 TensorDock offer spot instances for NVIDIA RTX A4000?▾
Yes, TensorDock offers spot/preemptible instances for NVIDIA RTX A4000, which can reduce costs by 50-80% compared to on-demand pricing. Spot instances are ideal for fault-tolerant workloads like batch inference, hyperparameter tuning, and training jobs with checkpointing. Note that spot instances can be interrupted when demand is high, so ensure your workflow can handle preemption gracefully.
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