ml

“SiMa.ai Raises $70M for Unveiling a Next-Generation, Multimodal GenAI Processor”

Krishna Rangasayee Sima Ai Founder
SiMa.ai, named after Seema, the Hindi word for “boundary,” strives to leverage this shift by offering its edge AI SoC to organizations across industrial manufacturing, retail, aerospace, defense, agriculture and healthcare sectors. As the demand for GenAI is growing, SiMa.ai is set to introduce its second-generation ML SoC in the first quarter of 2025 with an emphasis on providing its customers with multimodal GenAI capability. The new SoC will be an “evolutionary change” over its predecessor with “a few architectural tunings” over the existing ML chipset, Rangasayee said. It would work as a single-edge platform for all AI across computer vision, transformers and multimodal GenAI, the startup said. The second-generation chipset will be based on TSMC’s 6nm process technology and include Synopsys EV74 embedded vision processors for pre- and post-processing in computer vision applications.

Assisting Businesses with Offline Deployment of LLMs: Giga ML’s Solution

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In search of one, they founded Giga ML, a startup building a platform that lets companies deploy LLMs on-premise — ostensibly cutting costs and preserving privacy in the process. “Giga ML addresses both of these challenges.”Giga ML offers its own set of LLMs, the “X1 series,” for tasks like generating code and answering common customer questions (e.g. But it’s tough to say how X1 compares qualitatively; this reporter tried Giga ML’s online demo but ran into technical issues. Even if Giga ML’s models are superior in some aspects, though, can they really make a splash in the ocean of open source, offline LLMs? “Giga ML’s mission is to help enterprises safely and efficiently deploy LLMs on their own on-premises infrastructure or virtual private cloud,” Vummadi said.

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