Top 10 Serverless GPUs: A comprehensive vendor selection in '24

Large language models (LLMs) like chatGPT has been a hot topic for business world since last year. Thus, the number of these models have drastically increased. Yet, one major LLM challenge prevents more enterprises adopting it, system costs for developing these models. For instance, Megatron-Turing from NVIDIA and Microsoft is estimated to hold a cost of approximately $100 million for the entire project.

Serverless GPU space can reduce this cost by helping with inference phase of large language models (LLMs). Serverless computing can meet the computational requirements to run LLMs under a constant infrastructure.

In this article, we will define Serverless GPUs and compare top 10 providers in the emerging market.

Top 10 Serverless GPU providers

Companies are sorted alphabetically since this is an emerging domain and there is limited data available.

1.) Banana Dev

Banana Dev provides serverless GPU inference hosting for ML models. It offers Python framework to build API handlers, allowing users to run inference, connect data stores and call third-party APIs. With built-in CI/CD, Banana Dev converts apps into Docker images, deploying seamlessly on its serverless GPU infrastructure. Banana’s infrastructure handle traffic patterns swiftly and its autoscaling feature helps application scales dynamically based on demand. 

Pricing includes fixed and customized options for models like A100 40GB, A100 80GB, H100 80GB. Also, free trial is available for an hour.

2.) Baseten Labs

Baseten is a machine learning infrastructure platform for deploying models of various sizes and types efficiently, at scale, and cost-effectively for production use. Baseten users can effortlessly deploy a foundational model from the model library. Additionally, Baseten leverages GPU instances like A100, A10, and T4 to enhance computational performance.

Baseten also introduces an open-source tool called Truss, designed to help developers deploy AI/ML models in real-world scenarios. With Truss, developers can:

• Easily package and test model code, weights, and dependencies using a model server. 
• Develop their model with quick feedback from a live reload server, avoiding complex Docker and Kubernetes configurations.
• Accommodate models created with any Python framework, be it transformers, diffusors, PyTorch, Tensorflow, XGBoost, sklearn, or even entirely custom models.

3.) Beam Cloud

Beam, formerly known as Slai, provides easy REST API deployment with built-in features like authentication, autoscaling, logging, and metrics. Beam users can:

• Execute GPU-based long-running training tasks, choosing between one-time or scheduled automated retraining
• Deploy functions to a task Queue with automated retries, callbacks, and task status querie
• Customize autoscaling rules, granting control over maximum user waiting times.

4.) Cerebrium AI

Cerebrium AI offers a diverse selection of GPUs, including H100’s, A100’s, A5000’s,with a total of over 8 GPU types available. Cerebrium allows users to define their environment with infrastructure-as-code and direct access to code without the need for S3 bucket management.

5.) Fal AI

FAL AI delivers ready-to-use models with an API endpoints to customize and integrate to customer apps. Their platform supports Serverless GPUs, such as A100 and T4.

6.) Modal Labs

Modal labs platform is to run GenAI models, large scale batch jobs and job queues, providing serverless GPU models like Nvidia A100, A10G T4 and L4.

7.) Mystic AI

Mystic AI’s serverless platform is pipeline core which hosts ML models through an inference API. Pipeline core can create custom models with over 15 options, such as: GPT, Stable diffusion, and Whisper. Some of the features Pipeline core provides include:

• Simultaneous model versioning and monitoring
• Environment management, including libraries and frameworks
• Auto-scale across various cloud providers
• Support online, batch, and streaming inference
• East integrations with other ML and infrastructure tools.

Mystic AI also provides an active Discord community for support.

8.) Replicate

Replicate’s platform supports custom and pre-trained machine learning models. The platform delivers a waitlist for open-source models and offers flexibility with a choice between Nvidia T4 and A100. The platform also includes an open-source library, COG, to facilitate model deployment.

9.) RunPod

Runpod delivers fully managed and scalable AI endpoints for diverse workloads and applications. It provides users with the option to choose between machines and serverless endpoints, employing a Bring Your Own Container (BYOC) approach. It includes features like GPU instances, serverless GPUs, and AI endpoints. Key features of the platform include:

• Providing servers for all user types
• A straightforward loading process that involves dropping a container link to pull a pod
• A credit-based payment and billing system rather than direct card billing.

10.) Workers AI

Cloudflare introduces Workers AI, a serverless GPU platform accessible via REST API designed for seamless and cost-effective execution of ML inferences. The platform incorporates open-source models covering diverse inference tasks, including:

• Text generation
• Automatic speech recognition
• Text classification
• Image classification.

Cloudflare also integrates its serverless GPU platform with Hugging face, which allows Hugging Face users to avoid infrastructure wrangling while improve Cloudflare’s model catalog. Also, Workers AI integrates with Vectorize, a vector database by Cloudflare addressing context or use case limitations during the training of large language models with a fixed dataset.

What are other cloud providers?

Top cloud providers such as Google, AWS and Azure provide Serverless functioning, which does not support GPU at the moment. Other providers like Scaleway or Coreweave delivers GPU inference but do not offer serverless gpus.

Find out more on cloud gpu providers and GPU market. 

What are the benefits of serverless GPU?

Serverless GPUs benefits include:

1. Cost Efficiency:Users only pay for the GPU resources they actually use, making it a cost-effective solution. Traditional server setups may require constant provisioning of resources, leading to potential underutilization and wasted costs.
2. Scalability:Serverless architectures automatically scale to handle varying workloads. This means that as the demand for resources increases or decreases, the infrastructure dynamically adjusts, providing scalability without manual intervention.
3. Simplified Management:Developers can focus more on writing code for specific functions or tasks, as the cloud provider handles server provisioning, scaling, and other infrastructure management tasks. This abstraction simplifies the development process and reduces the operational burden.
4. On-Demand Resource Allocation:Serverless GPU architectures allow applications to access GPU resources on demand, eliminating the need for managing and maintaining physical or virtual servers dedicated to GPU processing. Resources are allocated dynamically based on application requirements.
5. Flexibility:Developers have the flexibility to scale resources up or down based on the specific needs of their applications. This adaptability is particularly useful for workloads with varying computational requirements.
6. Enhanced Parallel Processing:GPU computing excels at parallel processing tasks. Serverless GPU architectures are well-suited for applications that require significant parallel computation, such as machine learning inference, data processing, and scientific simulations.

Serverless GPU for machine learning models

In traditional machine learning workflows, developers and data scientists often need to provision and manage dedicated servers or clusters with GPUs to handle the computational demands of training complex models. Serverless GPU for machine learning abstracts away the complexities of infrastructure management. Here’s an overview of how Serverless GPU is commonly used for ML models today:

1. Training Models: Serverless GPU facilitates machine learning model training by offering dynamic resource allocation for efficient training on extensive datasets. Developers benefit from on-demand resources without the hassle of managing dedicated servers.
2. Inference: Serverless GPU is crucial for model inference, making quick predictions on new data. Ideal for applications like image recognition and natural language processing, it ensures fast and efficient execution, especially during variable demand periods.
3. Real-time Processing: Applications requiring real-time processing, such as video analysis, leverage Serverless GPU. Dynamic resource scaling enables swift processing of incoming data streams, making it suitable for real-time applications across domains.
4. Batch Processing: Serverless GPU handles large-scale data processing tasks in ML workflows involving batch processing. This is essential for data preprocessing, feature extraction, and other batch-oriented machine learning operations.
5. Event-Driven ML Workflows: Serverless architectures are event-driven, responding to triggers or event, such as updating a model when new data becomes available or retraining a model in response to certain events.
6. Hybrid Architectures: Some ML workflows combine serverless and traditional computing resources. For instance, GPU-intensive model training transitions to a serverless environment for AI inference, optimizing resource utilization.

Further reading

Discover more on GPU:

• Cloud GPUs for Deep Learning: Availability& Price / Performance
• Top 10 Cloud GPU Providers

External sources

• 1. Banana.dev
• 2. Cerebrium.ai
• 3. Modal Labs
• 4. Cloudflera