RTX 3080 on RunPod
Visit RunPodRunPod offers the NVIDIA GeForce RTX 3080, a 10GB VRAM Ampere architecture GPU, as a cost-effective option for machine learning workloads. This consumer-grade card delivers high performance for inference and experimentation, with 8704 CUDA cores, 272 Tensor cores, and up to 29.77 TFLOPS FP32 throughput. RunPod's dual-tier model—Community Cloud for lowest costs and Secure Cloud for reliability—pairs with FlashBoot technology for pod deployment in under 90 seconds. Per-second billing and spot instances minimize expenses for bursty tasks like fine-tuning 7B LLMs or running Stable Diffusion. Ideal for ML engineers and data scientists seeking affordable access without long-term commitments, it supports serverless inference via templates for PyTorch, TensorFlow, and Hugging Face. While not enterprise-grade, its value shines in prototyping, enabling rapid iteration on memory-constrained models. Limitations include potential variability in Community pods and no native multi-GPU scaling on single RTX 3080 instances.
Why NVIDIA GeForce RTX 3080 on RunPod?
Choose RunPod for RTX 3080 due to its per-second billing and spot instances, slashing costs for intermittent ML workloads—often under $0.20/hour on spots. FlashBoot ensures near-instant starts, perfect for the GPU's strengths in inference and fine-tuning. Community Cloud offers rock-bottom prices for experimentation, while Secure Cloud adds isolation for sensitive data. RTX 3080's 10GB VRAM handles 7B models efficiently, complemented by RunPod's NVMe storage and Jupyter-ready templates. This combo excels over hyperscalers for cost-sensitive users, providing consumer GPU power without setup overhead, though datacenter GPUs may outperform in sustained training.
Live Pricing
Real-time NVIDIA GeForce RTX 3080 offers from RunPod
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 RunPod | NVIDIA GeForce RTX 3080 10GB VRAM | 10GB | 8 vCPU 50GB RAM | 🌍global | $0.17/GPU/hr | Sold Out | ||
RunPod | NVIDIA GeForce RTX 3080 Ti 12GB VRAM | 12GB | 8 vCPU 50GB RAM | 🌍global | $0.18/GPU/hr | Available |
Performance Notes
Expect RTX 3080 on RunPod to deliver near-native Ampere performance: ~30 TFLOPS FP32, strong for inference on 7-13B models or image generation. 10Gbps networking supports data transfers; pods include 25-100GB NVMe SSD. Single-GPU only for this SKU—no multi-GPU scaling. Community pods may vary in CPU/RAM (typically 2-8 vCPU, 4-16GB), potentially bottlenecking I/O-heavy tasks. Secure pods offer consistency. Benchmarks show 80-90% utilization in MLPerf inference; sustained loads may throttle due to consumer TDP (320W). Unknowns: exact pod variability—test empirically for production.
A leader in democratized GPU space offering serverless inference and cost-effective experimentation.
Best For
Unique Features
- Dual-tier model (Community vs. Secure)
- FlashBoot technology
VRAM
10GB
Architecture
Ampere
Tier
consumer
Platform Features
Getting Started
Launch RTX 3080 pods on RunPod in minutes via intuitive dashboard. Select Community or Secure Cloud, choose templates like Jupyter or AutoDL, and scale with per-second billing. Ideal for quick prototyping without infrastructure management.
Steps
- 1Create a RunPod account and add payment method.
- 2Navigate to 'Pods' > 'Deploy' and filter for RTX 3080.
- 3Select Community/Spot for cost savings or Secure for reliability.
- 4Pick a template (e.g., PyTorch, RunPod Fast Stable Diffusion).
- 5Click 'Deploy'—use FlashBoot for <90s startup; connect via SSH/ Jupyter.
Pro Tips
- Opt for spot instances in Community Cloud to cut costs by 50-70% for non-critical experiments.
- Leverage pre-built templates to skip CUDA setup; customize via Dockerfiles for workflows.
- Monitor via RunPod dashboard; terminate idle pods to avoid charges with per-second billing.
Frequently Asked Questions
What is RunPod's billing model for NVIDIA GeForce RTX 3080?▾
RunPod bills per-second for GPU instances including NVIDIA GeForce RTX 3080. 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 RunPod offer spot instances for NVIDIA GeForce RTX 3080?▾
Yes, RunPod offers spot/preemptible instances for NVIDIA GeForce RTX 3080, 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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