The newly launched NVIDIA microservices enable organizations to build and deploy AI applications locally while preserving control of their data. NIM supplies high-performing containers for proprietary and open model deployment while CUDA-X accelerates the production process.
NVIDIA announced its new Blackwell architecture at the GTC keynote. Blackwell is the Hopper architecture successor, featuring a 30x performance increase and a 25x cost and energy consumption decrease when training or performing real-time inference for LLMs of up to 10 trillion parameters.
During this talk, we will discuss the Kubernetes stack's drawbacks in the context of AI and show how dstack addresses them in the training and deployment of GenAI models.
Join the Data Phoenix webinar, where Dmytro Spodarets and guest Andrey Cheptsov (Founder & CEO at dstack) will discuss the drawbacks of the Kubernetes stack in the context of AI and show how dstack addresses these drawbacks in the training and deployment of GenAI models.
During this talk, we will discuss a comprehensive understanding of the evaluation processes for LLMs, particularly in the context of preparing these models for deployment in production environments.
Join the Data Phoenix webinar, where Dmytro Spodarets and guest Andrei Lopatenko will discuss a comprehensive understanding of the evaluation processes for LLMs, particularly in the context of preparing these models for deployment in production environments.
Latest AI News, Write for Data Phoenix, Milestones in Speaker Recognition, GNN in TensorFlow, Building an Advanced RAG System on Your Computer, Unlocking LLM’s Potential with RAG, Transformers from Scratch, RingAttention, InseRF, MagicVideo-V2, En3D and more.
We’re looking for a Research Director to help us manage and grow Runway’s Applied ML Research team to create cutting-edge machine learning tools for AI-based content creation.
We're hiring exceptional Software Engineers for the distributed training team at Anyscale, which is responsible for building and maintaining open source machine learning libraries widely adopted across the industry.
As an AI Engineer (a Member of Technical Staff role on our Forward Deployed team) you will work directly with customers to quickly understand their greatest problems and design and implement solutions using Large Language Models.
As a Decentralized Machine Learning Research Engineer, you will be collaborating with open-source communities while doing impactful machine learning research. You’ll be responsible for establishing collaborations with researchers and publishing amazing work with them.
We are looking for a driven and innovative ML software engineer as our founding AI Engineer to ideate, scope, and train custom models for previously unsolvable problems that deliver significant value for our customers and, in turn, their customers.