RTX 2080 on Salad
Visit SaladSalad's NVIDIA GeForce RTX 2080 offering delivers a Turing-architecture consumer GPU with 8GB GDDR6 VRAM to a decentralized cloud powered by residential nodes. This setup is noteworthy for providing enterprise-grade ML compute at consumer prices, targeting ML engineers and data scientists focused on cost-sensitive, large-scale batch jobs and fault-tolerant inference. The RTX 2080's 2944 CUDA cores, 368 Tensor cores, and RT cores enable efficient training of smaller models (e.g., Stable Diffusion fine-tunes, lightweight LLMs up to 7B params) and high-throughput inference. Salad's unique value lies in its residential GPU network, yielding the lowest per-second pricing—often under $0.10/hour for spot instances—and massive parallelism across thousands of nodes. Ideal for non-latency-critical workloads like hyperparameter sweeps or dataset preprocessing, it avoids datacenter premiums while supporting Docker-based deployments. Limitations include variable node reliability and consumer-grade networking, but fault-tolerant designs mitigate this effectively. For teams prioritizing TCO over consistency, this combo unlocks scalable AI experimentation.
Why NVIDIA GeForce RTX 2080 on Salad?
Choose Salad for the RTX 2080 when ultra-low costs and decentralization align with batch-oriented ML workflows. Salad's residential network drives pricing as low as $0.05-$0.15/hour via per-second billing and spot instances, far below datacenter providers, complementing the RTX 2080's strong value for memory-bound inference (8GB VRAM handles most vision/language models). The GPU's Turing Tensor cores excel in Salad's fault-tolerant environment, where jobs auto-retry across nodes. Unique advantages: no egress fees, global node diversity for 24/7 availability, and seamless scaling to 1000s of GPUs without reservations. This suits startups or researchers avoiding lock-in, leveraging consumer hardware reliability for non-interactive tasks—perfect when RTX 2080 perf (10+ TFLOPS FP16) meets needs without A100 premiums.
Live Pricing
Real-time NVIDIA GeForce RTX 2080 offers from Salad
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 Salad | NVIDIA GeForce RTX 2080 8GB VRAM | 8GB | 1 vCPU 1GB RAM 1GB Storage | 🌍global | $0.08/GPU/hr | Available | ||
Salad | NVIDIA GeForce RTX 2080 Ti 11GB VRAM | 11GB | 1 vCPU 1GB RAM 1GB Storage | 🌍global | $0.10/GPU/hr | Available |
Performance Notes
On Salad, expect RTX 2080 performance comparable to on-prem: ~13.4 TFLOPS FP32, ~53 TFLOPS FP16 with TensorRT, suitable for batch inference (e.g., 50-100 imgs/sec ResNet-50) or fine-tuning <10GB models. Residential nodes introduce variability—CPU/RAM often 8-16c/16-32GB, power capped ~250W. Network bandwidth is consumer-grade (100Mbps-1Gbps), fine for batch uploads but limits real-time. Storage: ephemeral SSDs (100GB+), with S3-compatible object store. Multi-GPU scaling works via job sharding but lacks NVLink; use DDP/PyTorch Distributed. Known strengths: consistent CUDA 11+ support. Unknowns: exact node homogeneity—monitor via Salad API for preemptions. Benchmark first for production.
A decentralized cloud using consumer GPUs for massive batch jobs and fault-tolerant inference.
Best For
Unique Features
- Lowest pricing via residential node network
- Decentralized consumer GPU network
VRAM
8GB
Architecture
Turing
Tier
consumer
Platform Features
Getting Started
Launching RTX 2080 instances on Salad is straightforward via their dashboard or CLI, optimized for containerized ML jobs. New users can spin up fault-tolerant batches in minutes, with per-second billing starting post-deployment. Focus on Docker images for NVIDIA Docker compatibility.
Steps
- 1Sign up at salad.com/cloud and verify email; add payment method for credits.
- 2Navigate to 'Jobs' dashboard, select 'Create Batch Job' and choose RTX 2080 GPU.
- 3Upload Docker image or specify repo (e.g., nvcr.io/nvidia/pytorch); configure command and env vars.
- 4Set spot/preemptible mode, resources (e.g., 1 GPU, 16GB RAM), and launch—jobs auto-scale.
- 5Monitor via dashboard/API; retrieve results from attached storage when complete.
Pro Tips
- Design jobs with checkpointing and retries using Salad's fault-tolerance API to handle node preemptions seamlessly.
- Opt for spot instances to slash costs 70-90%; pair with multi-node sharding for 1000+ GPU batches.
- Benchmark your workload first with a small job—RTX 2080 shines in FP16 inference but watch VRAM for large batches.
Frequently Asked Questions
What is Salad's billing model for NVIDIA GeForce RTX 2080?▾
Salad bills per-second for GPU instances including NVIDIA GeForce RTX 2080. 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 Salad offer spot instances for NVIDIA GeForce RTX 2080?▾
Yes, Salad offers spot/preemptible instances for NVIDIA GeForce RTX 2080, 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.
Related Pages
Rent NVIDIA GeForce RTX 2080
Atlantic.net vs Salad: GPU Cloud Comparison
AWS vs Salad: GPU Cloud Comparison
Cirrascale vs Salad: GPU Cloud Comparison
NVIDIA A100 PCIe 40GB on Salad - Pricing & Availability
NVIDIA A100 SXM4 80GB on Salad - Pricing & Availability
NVIDIA L40S on Salad - Pricing & Availability