Monster API launched its platform to offer developers access to GPU infrastructure and pre-trained AI models.
This is achieved through decentralized computing, which enables developers to create AI applications quickly and efficiently, potentially saving up to 90% compared to traditional cloud options.
The platform grants developers access to the latest AI models, such as Stable Diffusion, ‘out-of-the-box’ at a cheaper price than traditional cloud ‘giants’ like AWS, GCP, and Azure, according to the company.
By utilizing Monster API’s full stack, which includes an optimization layer, a compute orchestrator, extensive GPU infrastructure, and ready-to-use inference APIs, a developer can create AI-powered applications in mere minutes. Furthermore, they can fine-tune these large language models with custom datasets.
“By 2030, AI will impact the lives of 8 billion people. With Monster API, our ultimate wish is to see developers unleash their genius and dazzle the universe by helping them bring their innovations to life in a matter of hours,” said Saurabh Vij, CEO and co-founder of Monster API. “We eliminate the need to worry about GPU infrastructure, containerization, setting up a Kubernetes cluster, and managing scalable API deployments as well as offering the benefits of lower costs. One early customer has saved over $300,000 by shifting their ML workloads from AWS to Monster API’s distributed GPU infrastructure.”
Monster API’s no-code fine-tuning solution enables developers to improve LLMs. This is achieved by specifying hyperparameters and datasets, thus simplifying the development process. Developers have the ability to fine-tune open-source models such as Llama and StableLM, enhancing response quality for tasks like instruction answering and text classification. This approach allows for achieving response quality akin to that of ChatGPT.