Fireworks.ai set out to build a lightning-fast, affordable, and customizable generative artificial intelligence (AI) inference solution for its customers. With billions of parameters, foundation models require powerful, often costly compute resources as they’re put into production.
Reported impact
In addition to receiving technical guidance, has collaborated with the AWS team on marketing opportunities. customers can simplify deployment and billing by purchasing ’s generative AI platform-as-a-service through AWS Marketplace , a curated digital catalog that customers can use to find, buy, deploy, and manage third-party software, data, and services to build solutions and run their businesses.
Fireworks.ai set out to build a lightning-fast, affordable, and customizable generative artificial intelligence (AI) inference solution for its customers. With billions of parameters, foundation models require powerful, often costly compute resources as they’re put into production. From its inception, has collaborated closely with AWS teams to determine how best to optimize compute power for its highly demanding inference engine. At first, the company used Amazon EC2 P4d Instances , powered by NVIDIA A100 Tensor Core GPUs, which offer high performance for machine learning training and high-performance computing applications in the cloud. In July 2023, AWS announced Amazon EC2 P5 Instances, the next generation of Amazon EC2 P4 instances and the highest-performance GPU-based instances for deep learning and high performance computing applications. “AWS has the latest and greatest hardware,” Dzhulgakov says.
At first, the company used Amazon EC2 P4d Instances , powered by NVIDIA A100 Tensor Core GPUs, which offer high performance for machine learning training and high-performance computing applications in the cloud.
Fireworks.ai set out to build a lightning-fast, affordable, and customizable generative artificial intelligence (AI) inference solution for its customers.
From its inception, has collaborated closely with AWS teams to determine how best to optimize compute power for its highly demanding inference engine.
In fact, many in the founding team worked on PyTorch to empower developers in the building of fast, secure, affordable generative AI products.
The CEO and cofounder of , Lin Qiao, had led the development and productionization of the open-source, popular deep learning framework PyTorch while she served as senior director of engineering at Meta.
With billions of parameters, foundation models require powerful, often costly compute resources as they’re put into production.
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