RTX 3080 on Vast.ai
Visit Vast.aiVast.ai's NVIDIA GeForce RTX 3080 offering stands out in the GPU cloud marketplace by delivering high-performance Ampere architecture GPUs at the absolute lowest costs through its decentralized, peer-hosted model. With 10GB GDDR6X VRAM, the RTX 3080 excels in machine learning workloads like fine-tuning mid-sized language models, computer vision inference, and generative AI prototyping, offering strong FP32/FP16 performance (up to 30 TFLOPS FP32) at a consumer-tier price point. This combination is ideal for cost-conscious ML engineers and data scientists running distributed experiments, batch inference, or resource-intensive simulations without enterprise budgets. Key value propositions include per-hour billing starting under $0.20/hr, spot instances for up to 50% savings, and granular search filters such as DLPerf/$ (deep learning performance per dollar) to optimize for value. While host reliability varies, Vast.ai's marketplace enables rapid scaling across thousands of RTX 3080 instances worldwide, making it perfect for short-term, high-volume compute needs where cost trumps consistency.
Why NVIDIA GeForce RTX 3080 on Vast.ai?
Choose Vast.ai for RTX 3080 when prioritizing rock-bottom pricing and flexibility in a decentralized ecosystem. Vast.ai aggregates RTX 3080s from individual hosts, driving costs to $0.15-$0.30/hr—far below major clouds—via competitive bidding and spot auctions. This complements the RTX 3080's consumer-grade efficiency for ML tasks under 10GB VRAM, like Stable Diffusion or Llama fine-tuning. Unique advantages include DLPerf/$ filters for perf-optimized selection, on-demand multi-GPU rentals (up to 8x), and instant SSH/Jupyter access. Ideal for experimenters avoiding long-term commitments, though it suits bursty workloads better than latency-critical production due to host variability.
Live Pricing
Real-time NVIDIA GeForce RTX 3080 offers from Vast.ai
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 Vast.ai | 6×NVIDIA GeForce RTX 3080 Ti 12GB VRAM | 12GB | 8 vCPU 94GB RAM 1527GB Storage | Ukraine | $0.01/GPU/hr $0.04/hr total (6×) | Sold Out | ||
Vast.ai | 6×NVIDIA GeForce RTX 3080 Ti 12GB VRAM | 12GB | 8 vCPU 94GB RAM 1660GB Storage | Ukraine | $0.01/GPU/hr $0.04/hr total (6×) | Sold Out | ||
Vast.ai | NVIDIA GeForce RTX 3080 10GB VRAM | 10GB | 24 vCPU 31GB RAM 327GB Storage | Japan | $0.01/GPU/hr | Sold Out | ||
Vast.ai | NVIDIA GeForce RTX 3080 10GB VRAM | 10GB | 24 vCPU 31GB RAM 693GB Storage | Japan | $0.03/GPU/hr | Sold Out | ||
Vast.ai | 2×NVIDIA GeForce RTX 3080 10GB VRAM | 10GB | 64 vCPU 126GB RAM 561GB Storage | China | $0.03/GPU/hr $0.06/hr total (2×) | Sold Out |
Performance Notes
On Vast.ai, RTX 3080 delivers solid ML performance: ~29.8 TFLOPS FP32, 238 TFLOPS Tensor FP16, suitable for training/inference on models like BERT-large or lightweight diffusion. Network bandwidth varies (100Mbps-10Gbps by host), impacting distributed training; opt for verified high-bandwidth instances. NVMe storage (500GB-4TB) is common, with fast local SSDs aiding data loading. Multi-GPU scaling works via NCCL but depends on host topology—peer-to-peer often limited. DLPerf scores average 50-70 on Vast.ai benchmarks. Expect 80-95% of on-prem speeds; variability from consumer hosts means testing recommended. No guaranteed uptime, but 99%+ reliable hosts available via filters.
A decentralized marketplace for absolute lowest costs and distributed experiments.
Best For
Unique Features
- Granular search filters like DLPerf/$
- Decentralized marketplace
VRAM
10GB
Architecture
Ampere
Tier
consumer
Platform Features
Getting Started
Getting started with Vast.ai's RTX 3080 is straightforward: sign up, search the marketplace with filters, rent an instance, and launch your ML environment in minutes. Supports Docker, templates for PyTorch/TensorFlow, and direct SSH/VNC access for seamless workflows.
Steps
- 1Create a free Vast.ai account and add payment method for instant rentals.
- 2Search 'RTX 3080', filter by DLPerf/$, RAM (>=32GB), bandwidth, and spot price.
- 3Select a verified host, choose image (e.g., Ubuntu + CUDA 12), and click 'Rent'.
- 4Connect via SSH (keys auto-generated) or web console; install deps with apt/pip.
- 5Run benchmarks (e.g., MLPerf) to verify, then scale to multi-GPU if needed.
Pro Tips
- Prioritize 'verified' and 'high uptime' hosts with DLPerf/$ >50 for best value and reliability.
- Use spot instances for non-urgent jobs to save 30-50%; set auto-relaunch for resilience.
- Pre-load datasets to local NVMe via SFTP to minimize transfer times on variable networks.
Frequently Asked Questions
What is Vast.ai's billing model for NVIDIA GeForce RTX 3080?▾
Vast.ai bills per-hour for GPU instances including NVIDIA GeForce RTX 3080. Hourly billing means you pay for full hours even if your job completes mid-hour. Plan your workloads accordingly to maximize cost efficiency.
Does Vast.ai offer spot instances for NVIDIA GeForce RTX 3080?▾
Yes, Vast.ai 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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