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Salad - GPU Cloud

★3.9
💬2588
💲Paid

Salad - GPU Cloud is a cost-effective, distributed GPU cloud platform designed for AI/ML workloads. It offers scalable compute resources, secure deployment, and a container engine for easy application management. Users can access a large network of GPUs at significantly lower costs compared to traditional hyperscalers.

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Platform
web
AI inferenceAffordable cloudBatch processingCloud computingContainerizationDeep LearningDistributed computing

What is Salad - GPU Cloud?

Salad - GPU Cloud is a distributed GPU cloud platform offering affordable and scalable compute resources specifically tailored for AI/ML workloads. It enables users to save up to 90% on cloud costs compared to traditional hyperscalers by harnessing unused compute resources globally. The platform provides access to a vast network of GPUs, starting from $0.02/hour, making it ideal for AI inference, batch processing, molecular dynamics, and more.

Core Technologies

  • Distributed Computing
  • GPU Cloud
  • Containerization

Key Capabilities

  • Affordable GPU pricing
  • Scalable compute resources
  • Secure deployment
  • Container Engine
  • Virtual Kubelets

Use Cases

  • Deploy AI/ML production models at scale
  • Run image generation and voice AI applications
  • Perform computer vision tasks
  • Conduct data collection and batch processing
  • Simulate molecular dynamics
  • Develop and deploy language models

Core Benefits

  • Save up to 90% on cloud costs
  • Access to over 60,000 daily active GPUs
  • Flexible pricing with no pre-paid contracts
  • Secure and reliable platform with SOC2 certification
  • Sustainable computing by utilizing unused GPUs

Key Features

  • Distributed GPU cloud
  • Affordable GPU pricing
  • Scalable compute resources
  • Secure deployment
  • Container Engine
  • Virtual Kubelets

How to Use

  1. 1
    Containerize your AI/ML application
  2. 2
    Choose the required GPU resources
  3. 3
    Deploy on SaladCloud's distributed network
  4. 4
    Let SaladCloud manage the orchestration
  5. 5
    Monitor and scale your workloads as needed

Pricing Plans

RTX 5090

$0.294/hr (Batch)
32GB VRAM, 8GB Memory, 4 vCPUs

RTX 5080

$0.219/hr (Batch)
16GB VRAM, 8GB Memory, 4 vCPUs

RTX 4090

$0.204/hr (Batch)
24GB VRAM, 8GB Memory, 4 vCPUs

RTX 3090 Ti

$0.154/hr (Batch)
24GB VRAM, 8GB Memory, 4 vCPUs

RTX 3090

$0.124/hr (Batch)
24GB VRAM, 8GB Memory, 4 vCPUs

RTX 4080

$0.154/hr (Batch)
16GB VRAM, 8GB Memory, 4 vCPUs

RTX 4070 Ti

$0.124/hr (Batch)
12GB VRAM, 8GB Memory, 4 vCPUs

RTX 3080 Ti

$0.124/hr (Batch)
12GB VRAM, 8GB Memory, 4 vCPUs

RTX 3060

$0.084/hr (Batch)
12GB VRAM, 8GB Memory, 4 vCPUs

RTX 3080

$0.114/hr (Batch)
10GB VRAM, 8GB Memory, 4 vCPUs

RTX 3070 TI

$0.094/hr (Batch)
8GB VRAM, 8GB Memory, 4 vCPUs

RTX 3070

$0.094/hr (Batch)
8GB VRAM, 8GB Memory, 4 vCPUs

RTX 3060 Ti

$0.064/hr (Batch)
8GB VRAM, 8GB Memory, 4 vCPUs

General Purpose Instance

$0.005/hr
1GB Memory, 1 vCPU

CPU-Optimized Instance

$0.006/hr
2GB Memory, 1 vCPU

Memory-Optimized Instance

$0.012/hr
8GB Memory, 1 vCPU

Frequently Asked Questions

Q.What kind of GPUs does SaladCloud have?

A.SaladCloud uses RTX/GTX class GPUs from Nvidia, specifically onboarding AI-enabled, high-performance compute GPUs.

Q.How does security work on SaladCloud?

A.SaladCloud encrypts containers in transit and at rest, runs them in an isolated environment, and ensures a consistent compute environment.

Q.What are some unique traits of SaladCloud?

A.SaladCloud has longer cold start times and is subject to interruption due to its compute-sharing network. The highest vRAM available is 24 GB.

Q.How does SaladCloud work?

A.Workloads are deployed via Docker containers. SaladCloud handles orchestration, ensuring uninterrupted GPU time as per requirements.

Q.Do I pay for cold boot time?

A.No, you only pay for the time the hardware is available to your application, not for cold boot/start time.

Pros & Cons (Reserved)

✓ Pros

  • Up to 90% cost savings
  • Access to a large network of GPUs
  • Easy deployment with Salad Container Engine
  • Flexible pricing
  • Secure and reliable platform
  • Sustainable computing

✗ Cons

  • Longer cold start times
  • Subject to interruption
  • Maximum vRAM limited to 24 GB
  • Not suitable for extremely low-latency workloads

Alternatives

No alternatives found.