LLM
Cloud Inference: 3 Powerful Reasons to Use in 2025
Figure 1. Popularity of the keyword “cloud inference” on Google search engine worldwide in the past 5 years. Deep learning models achieve high accuracy in tasks like speech recognition and image classification, often surpassing human performance. However, they require large training datasets and significant computational power.
Cloud LLM vs Local LLMs: 3 Real-Life examples & benefits
In 2025, Cloud LLMs and Local LLMs are transforming business operations with unique advantages. Cloud LLMs, powered by advanced models like Grok 3, o3, and GPT-4.1, offer exceptional scalability and accessibility. Conversely, Local LLMs, driven by open-source models such as Qwen 3, Llama 4, and DeepSeek R1, ensure superior privacy and customization.
Large Multimodal Models (LMMs) vs LLMs in 2025
Large language models (LLMs) can handle textual tasks well but struggle with non-textual inputs, such as speech or video. In contrast, large multimodal models (LMMs) are emerging to handle various data types, including text, images, and audio. However, their technical complexity and data demands present potential challenges.
LLM Data Guide & 6 Methods of Collection in 2025
In the expanding AI and generative AI market (Figure 1), large language models (LLMs) have emerged as pivotal. These models empower machines to generate human-like content, heavily reliant on quality data. Here, we present a guide for business leaders on accessing and managing LLM data, offering insights into collection methods and data collection services.
Best RAG tools: Embedding Models, Libraries and Frameworks
Retrieval-Augmented Generation (RAG) is an AI method that improves large language model (LLM) responses by using external information sources. We benchmarked 4 embedding models and 3 chunk sizes to understand the practical performance of RAG systems. We examined how each parameter influences retrieval accuracy and response quality.
Best 10 Large Language Models in Healthcare in 2025
The healthcare industry with its vast amounts of patient data and medical literature, seeks efficient ways to use this information for better patient outcomes. By leveraging the capabilities of LLMs in healthcare processes, organizations can provide better patient care, research, and data privacy thanks to LLMs’ ability to generate, and summarize text-rich data.
Top 10 Vector Database Use Cases in 2025
Processing, storing, and retrieving vast amounts of information rapidly and efficiently is paramount for businesses. Vector databases are a critical emerging technology in addressing this demand. Unlike traditional databases, vector databases focus on high-dimensional vector data, offering unique advantages for certain use cases.
Top 40+ LLMOps Tools & Compare them to MLOPs in 2025
LLMs are growing rapidly, but development and fine-tuning remain expensive. .To better understand the landscape, we’ve also prepared a detailed comparison of LLMOps and MLOps tools to highlight how they differ in capabilities, focus areas, and workflows. LLMOps tools help reduce these costs by streamlining LLM management.
Using Vector Databases for LLMs: Applications and Benefits
Vector databases (VDBs) and large language models (LLMs) like GPT series are gaining significance. Data and computational advancements drive technological trends. Considering the role of vector databases in GenAI applications, their significance and interplay should not be understated. While generative AI like LLMs attracts attention, their supporting infrastructure often remains unnoticed.
Meta's New Llama 3.1 AI Model: Use Cases & Benchmark in 2025
Meta published model weights for Llama 3.1 which is one of the most advanced language models. This access enables enterprises, researchers or individual developers to finetune and deploy their own Llama-based models.