Serverless GPUs: Effortless Infrastructure that scales with you

Deploy your machine learning models on serverless GPUs in minutes

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How Inferless is approaching the model deployement process

Hassle free resource management

Using GPUs effectively could be challenging as Kubernetes doesn't allow for GPU sharing and GPUs are not as elastic as CPUs. We take care of autoscaling and latency to ensure efficient utilization of resources.

Balance autoscaling and latency

Through our proprietary algorithm, we help companies get the desired latency and linear autoscaling by using a cluster of always-on machines to optimize model load while maintaining the SLA.

Keep inference cost under control

With our developer-friendly usage-based billing module, we help companies only pay for the inference seconds they use for each model so that they don’t have to worry about fixed inference costs.

End-to-End Model Deployment

With us, engineering teams can just deploy the model file, along with the pre-processing and post-processing functions. We automatically create the endpoints and provide the monitoring data.

Why Serverless GPUs?

Viable Unit Economics

10x Cost Savings for Deployment

Gain cost efficiency by paying for GPU resources you need (utilization time) rather than expensive "always on" GPU resources.

Speed to market

Deploy Any Model with 3 Lines of Code

We can provide Serverless GPUs for any model architecture. Allow you to pair models from Hugging face, Sagemaker, Pytorch, Tensorflow.

Upgrade customer experience

Reduce Model Cold Start by 99%

Generally Out-of-the-box GPT-J model takes 25 minutes to “cold start”. With Us, it takes ~10 seconds. No hassle to provision hardware upfront

Simply Run and Push with Our Serverless GPUs

Run

With us, you can execute complex machine learning models with just a few lines of code, eliminating the need for in-depth knowledge of ML concepts.

Push

We've made the process easy for you so that you do not have to focus on GPU provisioning, intricating docker files

Run

With us, you can execute complex machine learning models with just a few lines of code, eliminating the need for in-depth knowledge of ML concepts.

Push

We've made the process easy for you so that you do not have to focus on GPU provisioning, intricating docker files