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NVIDIA is pushing the boundaries of language models and AI training through several innovative approaches. One notable advancement is Hymba, a family of small language models developed by NVIDIA research. Hymba uniquely combines transformer attention mechanisms with state space models, resulting in improved efficiency and performance. This hybrid-head architecture allows the models to harness both the high-resolution recall of attention and the efficient context summarization of SSMs, increasing the model’s flexibility.
An NVIDIA research team proposes Hymba, a family of small language models that blend transformer attention with state space models, which outperforms the Llama-3.2-3B model with a 1.32% higher average accuracy, while reducing cache size by 11.67× and increasing throughput by 3.49×. The integration of learnable meta tokens further enhances Hymba's capabilities, enabling it to act as a compressed representation of world knowledge and improving performance across various tasks. These advancements highlight NVIDIA's commitment to addressing the limitations of traditional transformer models while achieving breakthrough performance with smaller, more efficient language models.
Lambda is honored to be selected as anNVIDIAPartner Network (NPN) 2025 Americas partner of the year award winner in the category of Healthcare. Artificial intelligence systems designed for physical settings require more than just perceptual abilities—they must also reason about objects, actions, and consequences in dynamic, real-world environments. Researchers from NVIDIA introduced Cosmos-Reason1, a family of vision-language models developed specifically for reasoning about physical environments. NVIDIA, a global leader in AI and accelerated computing, is transforming this field by applyingartificial intelligence (AI)techniques, includinglarge language models(LLMs), to analyze and interpret biological data.
References :
An NVIDIA research team proposes Hymba, a family of small language models that blend transformer attention with state space models, which outperforms the Llama-3.2-3B model with a 1.32% higher average accuracy, while reducing cache size by 11.67× and increasing throughput by 3.49×. The integration of learnable meta tokens further enhances Hymba's capabilities, enabling it to act as a compressed representation of world knowledge and improving performance across various tasks. These advancements highlight NVIDIA's commitment to addressing the limitations of traditional transformer models while achieving breakthrough performance with smaller, more efficient language models.
Lambda is honored to be selected as anNVIDIAPartner Network (NPN) 2025 Americas partner of the year award winner in the category of Healthcare. Artificial intelligence systems designed for physical settings require more than just perceptual abilities—they must also reason about objects, actions, and consequences in dynamic, real-world environments. Researchers from NVIDIA introduced Cosmos-Reason1, a family of vision-language models developed specifically for reasoning about physical environments. NVIDIA, a global leader in AI and accelerated computing, is transforming this field by applyingartificial intelligence (AI)techniques, includinglarge language models(LLMs), to analyze and interpret biological data.
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References :
- lambdalabs.com: Lambda Honored to Accelerate AI Innovation in Healthcare with NVIDIA
- MarkTechPost: This AI Paper from NVIDIA Introduces Cosmos-Reason1: A Multimodal Model for Physical Common Sense and Embodied Reasoning.
Classification:
- HashTags: #NVIDIA #LanguageModels #AIResearch
- Company: NVIDIA
- Target: AI Community
- Product: NVIDIA DNA LLM
- Feature: Language Models
- Type: AI
- Severity: Informative