Tensorfuse provides a platform for deploying and managing large language model (LLM) pipelines on cloud infrastructure, allowing users to run serverless GPUs on AWS, Azure, or GCP. The solution enables businesses to scale generative AI models efficiently while keeping data secure within their private cloud, eliminating idle costs and reducing egress charges.
Funding
$500K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Deploying and scaling large language models (LLMs) requires significant infrastructure investment and specialized expertise, leading to high costs and complexity for businesses. Managing GPU resources across different cloud providers and ensuring data security within private clouds adds further overhead.
Solution
Tensorfuse offers a platform for deploying and autoscaling generative AI models on existing cloud infrastructure, eliminating idle costs and reducing egress charges. The platform allows users to run serverless GPUs on AWS, Azure, or GCP, paying only for the resources consumed. By connecting a cloud account to Tensorfuse, the platform automatically provisions the necessary resources and manages the infrastructure, enabling businesses to scale LLM pipelines efficiently while maintaining data privacy. The Tensorfuse SDK facilitates model deployment to a private cloud, providing an OpenAI-compatible API for easy integration.
Target Audience
Tensorfuse targets businesses that need to deploy and scale generative AI models efficiently on their own cloud infrastructure, including those using AWS, Azure, or GCP.
Features
- Serverless GPU deployment on AWS, Azure, and GCP
- Autoscaling of GPU workers from zero to hundreds in seconds
- OpenAI-compatible endpoint for easy integration with existing applications
- Support for custom container images and hardware specifications defined in Python
- Optimized container system for fast cold boots
- Centralized dashboard for logging and monitoring
- Integration with dev containers
- Secure data handling within the user's private cloud