AI Development Inference Engine Large Language Models 
Marktechpost
OCTOBER 18, 2024
Recent advancements in Large Language Models (LLMs) have reshaped the Artificial intelligence (AI)landscape, paving the way for the creation of Multimodal Large Language Models (MLLMs). Don’t Forget to join our 50k+ ML SubReddit.
NVIDIA
APRIL 9, 2024
Teams from the companies worked closely together to accelerate the performance of Gemma — built from the same research and technology used to create Google DeepMind’s most capable model yet, Gemini — with NVIDIA TensorRT-LLM , an open-source library for optimizing large language model inference, when running on NVIDIA GPUs.
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Unite.AI
SEPTEMBER 24, 2024
NVIDIA Inference Microservices (NIM) and LangChain are two cutting-edge technologies that meet these needs, offering a comprehensive solution for deploying AI in real-world environments. Understanding NVIDIA NIM NVIDIA NIM, or NVIDIA Inference Microservices, is simplifying the process of deploying AI models.
Marktechpost
OCTOBER 18, 2024
As a result, the potential for real-time optimization of agentic systems could be improved, slowing their progress in real-world applications like code generation and software development. The lack of effective evaluation methods poses a serious problem for AI research and development.
Unite.AI
JULY 18, 2024
Language Processing Units (LPUs): The Language Processing Unit (LPU) is a custom inference engine developed by Groq, specifically optimized for large language models (LLMs). LPUs use a single-core architecture to handle computationally intensive applications with a sequential component.
Unite.AI
JUNE 21, 2024
According to NVIDIA's benchmarks , TensorRT can provide up to 8x faster inference performance and 5x lower total cost of ownership compared to CPU-based inference for large language models like GPT-3. For instance, while the latest NVIDIA driver (545.xx) xx) supports CUDA 12.3,
Marktechpost
OCTOBER 21, 2024
As large language models (LLMs) become increasingly capable and better day by day, their safety has become a critical topic for research. To create a safe model, model providers usually pre-define a policy or a set of rules. In various cases, a standard one-size-fits-all safe model is too restrictive to be helpful.
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