SaladCloud
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About
SaladCloud is a community cloud platform that offers affordable and scalable GPU computing resources. It leverages unused compute power from consumer GPUs worldwide, creating a large, distributed network. Users can deploy AI/ML models at scale, saving up to 90% on compute costs compared to traditional hyperscalers. SaladCloud provides a fully managed container service, simplifying deployment and management. It is suitable for various GPU-heavy workloads like image generation, voice AI, computer vision, and language models. The platform prioritizes security, with several layers of protection to safeguard data. GPU owners earn rewards for sharing their resources, creating a sustainable and community-driven ecosystem.
Platform
Task
Features
• general purpose instances
• gpu instances
• optimized usage fees
• on-demand elasticity
• multi-cloud compatible
• global edge network
• massively scalable orchestration engine
• affordable gpu cloud computing
FAQs
What is SaladCloud?
SaladCloud is the world’s largest distributed cloud network with 1000s of consumer GPUs at the lowest cost. Our cloud is powered by unused latent compute shared by individuals & businesses around the world.
What is Salad Container Engine (SCE)?
Workloads are deployed to SaladCloud via docker containers. SCE is a massively scalable orchestration engine, purpose-built to simplify this container development. Containerize your model and inference server, choose the hardware and we take care of the rest.
How big is the SaladCloud Compute Network?
Over 2 Million+ GPU owners in 190+ countries are part of the SaladCloud ecosystem. 450,000+ GPUs having contributed significant compute and everyday, 11,000+ GPUs are active on the network.
What kind of GPUs does SaladCloud have?
All GPUs on SaladCloud belong to the RTX/GTX class of GPUs from Nvidia. Our GPU selection policy is strict and we only onboard AI-enabled, high performance compute capable GPUs to the network.
How does SaladCloud work?
GPUs on SaladCloud are similar to spot instances. On one side, we have GPU owners who contribute their resources to SaladCloud when not in use. Some providers share GPUs for 20-22 hours a day. Others share GPUs for 1-2 hours per day. Users running workloads on SaladCloud select the GPU types and quantity they need. SaladCloud handles all the orchestration in the backend and ensures you will have uninterrupted GPU time as per requirements.
How does security work on Salad?
Every day, 100s of businesses run production workloads on SaladCloud securely. We have several layers of security to keep your containers safe, encrypting them in transit, and at rest. Containers run in an isolated environment on our nodes - keeping your data isolated and also ensuring you have the same compute environment regardless of the machine you’re running on.
Why do owners share GPUs with Salad?
Owners earn rewards (in the form of Salad balance) for sharing their compute. Many compute providers earn 100−100 - 100−200 per month on SaladCloud as a reward that they exchange for games, gift cards and more.
What if a host tries to access my container?
Our constant host intrusion detection tests look for operations like folder access, opening a shell, etc. If a host machine tries to access the linux environment, we automatically implode the environment and blacklist the machine. We’re also bringing Falco into our runtime for a more robust set of checks.
What are some unique traits of SaladCloud compared to other clouds?
Since SaladCloud is a compute-share network, our GPUs have longer cold start times than usual, and are subject to interruption. We only have RTX/GTX class of GPUs from Nvidia. Our thesis is that most AI/ML production workloads get better cost-performance on consumer-grade GPUs. See our benchmarks for more information. The highest vRAM on the network is 24 GB. Workloads requiring extremely low latency times are not a fit for our network.
What are SaladCloud Endpoints/APIs?
SaladCloud has one API offering today - a full-featured Transcription API. We will be adding more APIs to our suite in the coming months.
How does latency work on Salad?
Since our GPUs are globally distributed, and often accessed via residential internet connections, latency can be higher on SaladCloud than on datacenter-based clouds. The best use cases for SaladCloud’s network are ones that DO NOT have extremely low latency requirements. Many companies run production workloads on SaladCloud with acceptable latency times. One of SaladCloud’s largest users is an AI image generation tool that serves millions of users. Our team can work with you on the right architecture to ensure your latency requirements are met.
How do I troubleshoot issues on Salad?
All SaladCloud users have access to logs by configuring external logging. Axiom is our preferred external logging service provider. Your logs are seamlessly transmitted to Axiom for troubleshooting. SaladCloud users can also view their logs directly in the portal. SaladCloud users can access a terminal in a running container instance.
What should I be aware of before deploying a workload to Salad?
Cold start time is high on SaladCloud. Give it a few minutes to let your containers up and running. Instances will be interrupted with no warning. Architect your application accordingly, such as retrying failed requests, and running multiple replicas to provide coverage during automatic fail-overs. Network performance will vary from node to node, due the distributed, residential nature of the network. For Mac developers, be sure to build your containers for amd64, not arm64.
What happens if a GPU goes offline?
Salad Container Engine automatically reallocates your workload to another GPU (same type and class) when a resource goes offline.
How can I be sure a GPU is performant?
We use a proprietary trust rating system to index node performance, forecast availability, and select the optimal hardware configuration for deployment. We also run proprietary tests on every GPU to determine their fit for our network. For more on performant GPU infrastructure configuration, view our [Build High-Performance Applications] tutorial.
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