Several tech companies, including NVIDIA, Databricks, and MosaicML, are collaborating to improve the performance of Large Language Models (LLMs) for inference. NVIDIA's GH200 chip is highlighted for its high chip-to-chip bandwidth, which benefits LLMs requiring CPU-offloading. Databricks has integrated TensorRT-LLM with its inference service, achieving state-of-the-art performance. The collaboration between Databricks, MosaicML, and NVIDIA aims to deliver high-performance LLM inference, with a focus on serving Mixtral from MistralAI and MoE. Open-source tools like LLMWare and Milvus are recommended for deploying RAG on-premises to protect data security and privacy.
I'm sure everyone wants to read about @databricks/@MosaicML inference stack over the holidays, so here ya go! Serving Mixtral from @MistralAI and MoE (in the works for some time): https://t.co/CILKaynbne Collaborating w/@nvidia and building upon TRT-LLM for inference:…
I'm sure everyone wants to read about @databricks/@MosaicML inference stack over the holidays, so here ya go! Serving Mixtral from @MistralAI and MoE (in the works for some time): https://t.co/CILKaynbne Collaborating w/@nvidia and building upon it for inference:…
Consistent high performance for #LLM inference is now table stakes. See how we're delivering #SOTA performance with @nvidia @NVIDIAAI at @databricks https://t.co/KvH9OAIhUp
For the last six months, we've been collaborating with @nvidia to integrate TensorRT-LLM with our inference service, achieving state-of-the-art inference performance. Read how we did it together and how you can benefit from our collab👇 https://t.co/qteVwFqPKg
GH200's high chip-to-chip bandwidth boosts applications requiring CPU-offloading. It's a game-changer for LLMs with Zero-inference and beyond. https://t.co/fuScEC0Itw
When working with LLMs, the right retrieval strategy and mechanisms are key if you want to protect data security and privacy. Take a look at how you can deploy #RAG on-prem using open-source tools like LLMWare and #Milvus: https://t.co/0oxsafuXuw with @AiBloks https://t.co/PfRnRL0YRS
Learn how to build advanced, structured retrieval over your semi-structured data with LLMs 👇 1️⃣ Setup auto-retrieval capabilities over a vector db (@pinecone) - take full advantage of semantic search + metadata filtering. 2️⃣ Observe all prompts/traces with @arizeai Phoenix 3️⃣… https://t.co/UmE5FSGh7e
When deploying LLM applications using RAG, it’s essential to consider GPU memory and bandwidth to unlock high-performance inference at scale. Learn how deploying #RAG applications on NVIDIA GH200 delivers accelerated performance. #DataCenter #GenerativeAI https://t.co/BQed9vAG3b
Combining the speed and advanced similarity searching provided by #MongoDB Atlas Vector search with the extraction and rich metadata filtering provided by @UnstructuredIO helps improve #LLM accuracy and determinism. Step through of how it works https://t.co/TmyNN9dXzC