Fastino, a new artificial intelligence foundation model developer, launched today to provide a family of task-optimized language models designed to maintain high performance and accuracy without the need to run on high-end graphics processing units.

The company also announced it raised $7 million in a pre-seed funding round led by Insight Partners and Microsoft Corp.’s M12 venture arm, with participation from NEA, Valor, GitHub Inc. Chief Executive Thomas Dohmke and others.

“Fastino aims to bring the world more performant AI with task-specific capabilities,” said Ash Lewis, co-founder and chief executive of Fastino. “Whereas traditional LLMs often require thousands of GPUs, making them costly and resource-intensive, our unique architecture requires only central processing units or neural processing units. This approach enhances accuracy and speed while lowering energy consumption compared to other large language models.”

The company said that its models are developed on a fit-for-purpose architecture for critical enterprise use cases and optimized for specific tasks, which makes them performant enough that they do not need to rely on heavyweight high-end GPUs. These use cases include structuring textual data, text summarization and task planning.

“This task-level approach allows us to focus on delivering exceptional performance for distinct use cases relative to generalized models,” Lewis told SiliconANGLE. “We achieve this by making architectural adjustments tailored to each task, which enables models that are not only highly performant but also faster and smaller than traditional generalized LLMs. “

According to Fastino, the company’s novel AI architecture can operate up to 1,000 times faster than traditional LLMs, allowing for flexible deployment across CPUs. Task optimization also allows for distributed AI systems, which makes them less vulnerable to security issues, such as adversarial attacks and privacy issues.

One limiting challenge many enterprise companies face when deploying LLMs is the significant energy usage of hundreds or thousands of GPUs. By using an AI model that only needs CPUs or NPUs for task-optimized use cases, it would greatly reduce the amount of energy needed.

The difference between a task-optimized language model and an LLM is that traditional LLMs are generalized and are not optimized for any particular capability. An LLM would be equally capable of question-and-answer, text generation, summarization, task planning, document analysis and more, making it a very large complex piece of software that requires a significant amount of computation. Task-specific optimization would make a particular language model very good at particular tasks, allowing it to be highly performant, accurate and fast for those activities.

“Global enterprises are facing increasing difficulty in accessing computing power while achieving the precision and speed necessary to integrate AI effectively,” said Fastinno co-founder and Chief Operating Officer George Hurn-Maloney. “Fastino aims to fix this with scalable, high-performance language models, optimized for enterprise tasks.”

Image: Shutterstock/Everything Possible