RTX 3090 on Salad
Visit SaladSalad's NVIDIA GeForce RTX 3090 offering provides ML engineers with access to a high-end consumer GPU featuring 24GB GDDR6X VRAM on the Ampere architecture. This combination stands out in Salad's decentralized residential network, which aggregates consumer hardware for the lowest market pricing on high-VRAM compute. Noteworthy for enabling cost-effective handling of memory-intensive workloads like fine-tuning 7B-70B LLMs, diffusion models, or large-batch inference without enterprise costs. Target audience includes startups, researchers, and production teams prioritizing affordability over guaranteed uptime. Key value propositions: per-second billing with spot instances yielding sub-$0.20/GPU-hour rates; fault-tolerant design for massive batch jobs resilient to node variability; global residential distribution reducing regional latency. While consumer-grade, it delivers ~35 TFLOPS FP32 performance, making it ideal for prototyping and scale-out tasks where 24GB VRAM unlocks larger models. Limitations include potential host variability and residential networking.
Why NVIDIA GeForce RTX 3090 on Salad?
Salad paired with the RTX 3090 excels for budget-conscious ML workloads leveraging its 24GB VRAM for memory-bound tasks like LLM inference or Stable Diffusion. Salad's decentralized residential network drives unique advantages: lowest pricing (spot instances often <$0.15/GPU-hr) via consumer-scale supply, per-second billing for precise cost control, and fault-tolerant orchestration suiting batch jobs tolerant of churn. This complements the 3090's Ampere strengths—high tensor core throughput for training/inference—without datacenter premiums. Ideal when enterprise providers like AWS/GCP charge 3-5x more for similar VRAM. Enables accessible high-end prototyping, though requires fault-tolerant designs to handle node diversity.
Live Pricing
Real-time NVIDIA GeForce RTX 3090 offers from Salad
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 Salad | NVIDIA GeForce RTX 3090 24GB VRAM | 24GB | 1 vCPU 1GB RAM 1GB Storage | 🌍global | $0.17/GPU/hr | Available | ||
Salad | NVIDIA GeForce RTX 3090 Ti 24GB VRAM | 24GB | 1 vCPU 1GB RAM 1GB Storage | 🌍global | $0.19/GPU/hr | Available |
Performance Notes
Expect RTX 3090 on Salad to deliver solid Ampere performance: ~35 TFLOPS FP32, 142 TFLOPS tensor FP16, suiting fine-tuning/inference on models fitting 24GB VRAM. Residential networking limits bandwidth to 100-1000Mbps—adequate for dataset syncing but not ultra-low-latency. Storage typically includes 250-1000GB NVMe SSDs per node, with S3-compatible object storage integration. Multi-GPU scaling via software (e.g., PyTorch DDP) possible across nodes, but no NVLink; expect 70-90% efficiency due to decentralized topology. Known variability (5-20% perf std dev from host power/thermals); benchmarks approximate datacenter 3090 at 80-95%. Unknowns: precise inter-node latency, max pool sizes.
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
24GB
Architecture
Ampere
Tier
consumer
Platform Features
Getting Started
Launching an RTX 3090 on Salad is simple via their intuitive web dashboard. New users sign up, fund via card/crypto, select from GPU marketplace, and deploy ML-ready images (Ubuntu/CUDA). Connect via SSH/VNC/Jupyter for workloads; spot instances start in minutes.
Steps
- 1Sign up at salad.com, verify email, and complete profile.
- 2Add payment method (credit card, PayPal, or crypto).
- 3Go to GPU Marketplace, filter for 'RTX 3090', select region.
- 4Configure instance: choose spot/on-demand, OS (Ubuntu 22.04), storage (min 250GB SSD).
- 5Launch and connect via SSH with provided key or web console.
Pro Tips
- Prioritize spot instances for 50-80% savings on fault-tolerant batch jobs; monitor interruptions via API.
- Use CUDA 11.8+ Docker images pre-loaded with PyTorch/TensorFlow to cut startup from 10min to <2min.
- Design workloads with checkpointing and Kubernetes for seamless multi-node scaling across residential hosts.
Frequently Asked Questions
What is Salad's billing model for NVIDIA GeForce RTX 3090?▾
Salad bills per-second for GPU instances including NVIDIA GeForce RTX 3090. 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 3090?▾
Yes, Salad offers spot/preemptible instances for NVIDIA GeForce RTX 3090, 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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