🔍 Explore AI21 Labs' Jamba 1.5, designed for diverse AI tasks like content creation and data insights. ➡️ https://nvda.ws/4fVqfmo Using transformer and Mamba architectures, this MoE model ensures top efficiency and extensive context handling. Experience Jamba 1.5 API as NVIDIA NIM microservice from the NVIDIA API catalog.
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This is how K8s dynamic resource provisioning should work. Smart Fabrics that understand how to convert a pod spec into a running solution with dynamic hardware.
We're thrilled to showcase our joint collaboration with NVIDIA and Supermicro at SuperCompute 24 in Atlanta 📍 | Nov 17-22 | Booth #1943! Experience Inference-as-a-Service on Composable Kubernetes and discover how to unlock the power of AI with NIM™ inference microservices, enabling flexible and scalable GPU deployments. Our latest tech leverages dynamically configurable Kubernetes clusters with composable infrastructure to transform container deployment and management. Don’t miss this opportunity to see how we are redefining scalable, efficient AI workloads. 🚀 The Age of Autonomous AI has Arrived 🚀 Download the Full White Paper here: https://lnkd.in/g9MSWrWD
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Nvidia Software Talent Sourcer sourcing for Sys. GPU&SoC, Prin Dist ML, Sr Sys SWE Spark Service, Prin Sys SW, Sr Sys SW-Signing Services, Sr. Solution vGPU&Omniverse, Linux GPU Sys SW Mgr, Sys SW-Enterprise Engineers.
🔍 Explore AI21 Labs' Jamba 1.5, designed for diverse AI tasks like content creation and data insights. Using transformer and Mamba architectures, this MoE model ensures top efficiency and extensive context handling. Experience Jamba 1.5 API as NVIDIA NIM microservice from the NVIDIA API catalog.
Jamba 1.5 LLMs Leverage Hybrid Architecture to Deliver Superior Reasoning and Long Context Handling | NVIDIA Technical Blog
developer.nvidia.com
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Head of Financial Technology and Lead DevRel Banking (EMEA) @ NVIDIA | Accelerated AI/GenAI Compute | Keynote, Panel, Moderator, Editor | AI in FSI | AI in ESG, ClimateTech, RiskTech | Trustworthy AI |
🔍 Explore AI21 Labs' Jamba 1.5, designed for diverse AI tasks like content creation and data insights. Using transformer and Mamba architectures, this MoE model ensures top efficiency and extensive context handling. Experience Jamba 1.5 API as NVIDIA NIM microservice from the NVIDIA API catalog.
Jamba 1.5 LLMs Leverage Hybrid Architecture to Deliver Superior Reasoning and Long Context Handling | NVIDIA Technical Blog
developer.nvidia.com
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🔍 Explore AI21 Labs' Jamba 1.5, designed for diverse AI tasks like content creation and data insights. Using transformer and Mamba architectures, this MoE model ensures top efficiency and extensive context handling. Experience Jamba 1.5 API as NVIDIA NIM microservice from the NVIDIA API catalog.
Jamba 1.5 LLMs Leverage Hybrid Architecture to Deliver Superior Reasoning and Long Context Handling | NVIDIA Technical Blog
developer.nvidia.com
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Nvidia Software Talent Sourcer sourcing for Sys. GPU&SoC, Prin Dist ML, Sr Sys SWE Spark Service, Prin Sys SW, Sr Sys SW-Signing Services, Sr. Solution vGPU&Omniverse, Linux GPU Sys SW Mgr, Sys SW-Enterprise Engineers.
🔍 Explore AI21 Labs' Jamba 1.5, designed for diverse AI tasks like content creation and data insights. Using transformer and Mamba architectures, this MoE model ensures top efficiency and extensive context handling. Experience Jamba 1.5 API as NVIDIA NIM microservice from the NVIDIA API catalog.
Jamba 1.5 LLMs Leverage Hybrid Architecture to Deliver Superior Reasoning and Long Context Handling | NVIDIA Technical Blog
developer.nvidia.com
-
🔍 Explore AI21 Labs' Jamba 1.5, designed for diverse AI tasks like content creation and data insights. Using transformer and Mamba architectures, this MoE model ensures top efficiency and extensive context handling. Experience Jamba 1.5 API as NVIDIA NIM microservice from the NVIDIA API catalog.
Jamba 1.5 LLMs Leverage Hybrid Architecture to Deliver Superior Reasoning and Long Context Handling | NVIDIA Technical Blog
developer.nvidia.com
-
🔍 Explore AI21 Labs' Jamba 1.5, designed for diverse AI tasks like content creation and data insights. Using transformer and Mamba architectures, this MoE model ensures top efficiency and extensive context handling. Experience Jamba 1.5 API as NVIDIA NIM microservice from the NVIDIA API catalog.
Jamba 1.5 LLMs Leverage Hybrid Architecture to Deliver Superior Reasoning and Long Context Handling | NVIDIA Technical Blog
developer.nvidia.com
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🚀Check out my simple implementation of #beyondllm, a new open-source #RAG framework by AI Planet, built on top of LlamaIndex. Here, I customized #beyondllm to use llama3.1 8b and NVIDIA's nv-embed-v1, both packaged as NVIDIA NIM™ inference microservices. #beyondllm simplifies RAG devs 'with just 5-7 lines of code' 🔥 The framework integrates components like #source and #auto_retriever and supports advanced evaluation metrics with the RAG Triad Evaluations of: 👉Context relevancy score 👉Answer relevancy score 👉Groundness score
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🚀 Learn more about Unit8 GenAI HyperScaler key features for speeding up GenAI projects! . . .
🚀 Learn more about Unit8 GenAI HyperScaler key features for speeding up GenAI projects! Following our recent announcement of Unit8 GenAI HyperScaler - the new, advanced version of our #GenAI Accelerator, let’s take a closer look at the key features: • Product-Grade Private GenAI Services • Modular & Containerized Architecture • Nvidia NIM Compatibility • Versatile Data Connectors • Out-of-the-box #Evaluation Tooling • Blueprints for Advanced GenAI Use Cases More importantly, Unit8 GenAI HyperScaler #accelerates your GenAI journey by several months of work. The first results can be seen after just two weeks! Learn more about the details in the document below and visit our website to explore the potential to deliver on GenAI use cases. 👉 https://lnkd.in/dYP72Sww
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We are honored to have our work recognized by Adrian Cockcroft at #sw2con. Just as LLMs are capable of reasoning, they can also hallucinate or follow less probable paths of reasoning. General models attempt to address these issues through methods like Chain of Thought, Tree of Thought or an endless loop of verification cycles. However, these approaches still result in brittle systems that are prone to latency. Therefore, it's crucial to have an additional abstraction layer serving as a controller (director) and specialized actors (domain-trained LLMs) equipped with tools to achieve better results. Learn more about our approaches here: https://lnkd.in/dP3n23-U Our technical report: https://lnkd.in/gR6RMzTs
PR & AR, Putting Early Stage Tech Brands on the Map from VMware to HashiCorp to Chronosphere to Tecton to Flip AI
Exceptional “Thanks For The Memory: New GPU Architectures and Workloads” keynote by Adrian Cockcroft - he’s highlighting Flip AI, Corey Harrison, Sunil Mallya and Deap Ubhi’s observability company, here #sw2con
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