Kevin Guo’s Post

Hive is thrilled to announce the integration of our proprietary AI models with NVIDIA NIM. Our groundbreaking collaboration allows Hive customers to deploy our industry-leading models in private clouds and on-premises data centers for the first time—critical in cases where data governance or other factors challenge the use of cloud-based APIs. The first Hive models to be made available with NIM are our AI-generated content detection models. We plan to release additional models on the platform in the coming months. We are also announcing that internet social platforms can receive no-cost, 90-day trial access to our AI detection models, optimized by NVIDIA NIM, for the remainder of 2024. To learn more about our integration and how you can access Hive’s models with NVIDIA NIM, read the press release (https://lnkd.in/d-McFy4X) and our blog post (https://lnkd.in/d4Nd4RGg). #ai #generativeai #GenAI #nvidia

Hive is thrilled to announce the integration of our proprietary AI models with NVIDIA NIM. Our groundbreaking collaboration allows Hive customers to deploy our industry-leading models in private clouds and on-premises data centers for the first time—critical in cases where data governance or other factors challenge the use of cloud-based APIs. The first Hive models to be made available with NIM are our AI-generated content detection models. We plan to release additional models on the platform in the coming months. We are also announcing that internet social platforms can receive no-cost, 90-day trial access to our AI detection models, optimized by NVIDIA NIM, for the remainder of 2024. To learn more about our integration and how you can access Hive’s models with NVIDIA NIM, read the press release (https://lnkd.in/d-McFy4X) and our blog post (https://lnkd.in/d4Nd4RGg). #ai #generativeai #GenAI #nvidia

One of NVIDIA's keynote announcements at our GTC conference was the introduction of NIM (NVIDIA Inference Microservice). Customers across industries have been challenged by escalating inference costs for GenAI applications. Linked below is one of the first public reports on the impact of NIM. The results are outstanding and critical to furthering GenAI adoption across industries, especially financial services: 1. 30% improvement in accuracy of AI-generated responses 2. 40% reduction in inference costs and 60% decline in data preprocessing expenses while maintaining the same level of accuracy. 3. Latency was reduced by ~80%, ensuring near real-time responses for end users across various applications such as commerce, care, and operations. Read on for more details and message me to discuss how NVIDIA's accelerated computing platform can propel your AI transformation... #genai #financialservices #aiforbanking #aiforpayments #aifortrading https://lnkd.in/ehAmkx-J

🚀 Exciting news! #Cohesity adopts NVIDIA NIM to supercharge retrieval augmented generation apps for enterprises. 🚀 This collaboration enhances #AI capabilities: 1️⃣ Faster deployment of generative AI models 2️⃣ Streamlined development with prebuilt microservices 3️⃣ Accelerated AI innovation for your enterprise

Retrieval-augmented generation (RAG) makes large language models (LLMs) safer by grounding their outputs in factual information from specified external sources. This helps to address the limitations of LLMs, which can sometimes generate creative text formats that may not be entirely accurate. RAG helps to ensure the factual accuracy of LLM outputs, making them more reliable for real-world applications. Cohesity’s GAIA uses RAG on the protected stored data after its indexed and classified to produce results specific to your data

🚀 Exciting news! #Cohesity adopts NVIDIA NIM to supercharge retrieval augmented generation apps for enterprises. 🚀 This collaboration enhances #AI capabilities: 1️⃣ Faster deployment of generative AI models 2️⃣ Streamlined development with prebuilt microservices 3️⃣ Accelerated AI innovation for your enterprise

🚀 Exciting news! #Cohesity adopts NVIDIA NIM to supercharge retrieval augmented generation apps for enterprises. 🚀 This collaboration enhances #AI capabilities: 1️⃣ Faster deployment of generative AI models 2️⃣ Streamlined development with prebuilt microservices 3️⃣ Accelerated AI innovation for your enterprise

🚀 Exciting news! #Cohesity adopts NVIDIA NIM to supercharge retrieval augmented generation apps for enterprises. 🚀 This collaboration enhances #AI capabilities: 1️⃣ Faster deployment of generative AI models 2️⃣ Streamlined development with prebuilt microservices 3️⃣ Accelerated AI innovation for your enterprise

Empower generative AI inference performance using NVIDIA NIM on OCI How to choose the right inference engine You must consider several factors when choosing the right inference engine for a GenAI workload, including the efficiency, scalability, business value, complexity of deployment, maintenance, managing the inference engine, and the need for timely technical support. However, enterprises primarily focus on two key aspects: Total cost of ownership (TCO), measured by cost per token, and end-user experience, assessed by time to first token (TTFT) and inter-token latency (ITL). To address this complex trade-off, we ran the performance benchmark on a leading open source inference engine against NVIDIA NIM microservices. Oracle NVIDIA #ai #inference #NvidiaNIM https://lnkd.in/gD9aRxAk

🚀 Exciting news! #Cohesity adopts NVIDIA NIM to supercharge retrieval augmented generation apps for enterprises. 🚀 This collaboration enhances #AI capabilities: 1️⃣ Faster deployment of generative AI models 2️⃣ Streamlined development with prebuilt microservices 3️⃣ Accelerated AI innovation for your enterprise