LLM
Together AI Introduces Flexible Benchmarking for LLMs
Together AI unveils Together Evaluations, a framework for benchmarking large language models using open-source models as judges, offering customizable insights into model performance.
NVIDIA's NeMo Framework Enables Weekend Training of Reasoning-Capable LLMs
NVIDIA introduces an efficient method to train reasoning-capable language models over a weekend using the NeMo framework, leveraging the Llama Nemotron dataset and LoRA adapters.
Optimizing LLM Inference with TensorRT: A Comprehensive Guide
Explore how TensorRT-LLM enhances large language model inference by optimizing performance through benchmarking and tuning, offering developers a robust toolset for efficient deployment.
Enhancing LLM Workflows with NVIDIA NeMo-Skills
NVIDIA's NeMo-Skills library offers seamless integration for improving LLM workflows, addressing challenges in synthetic data generation, model training, and evaluation.
Understanding the Emergence of Context Engineering in AI Systems
Discover the rise of context engineering, a crucial component in AI systems that ensures effective communication and functionality for large language models (LLMs).
Optimizing LLM Inference Costs: A Comprehensive Guide
Explore strategies for benchmarking large language model (LLM) inference costs, enabling smarter scaling and deployment in the AI landscape, as detailed by NVIDIA's latest insights.
NVIDIA Introduces High-Performance FlashInfer for Efficient LLM Inference
NVIDIA's FlashInfer enhances LLM inference speed and developer velocity with optimized compute kernels, offering a customizable library for efficient LLM serving engines.
Together AI Launches Cost-Efficient Batch API for LLM Requests
Together AI introduces a Batch API that reduces costs by 50% for processing large language model requests. The service offers scalable, asynchronous processing for non-urgent workloads.
NVIDIA Introduces EoRA for Enhancing LLM Compression Without Fine-Tuning
NVIDIA unveils EoRA, a fine-tuning-free solution for improving compressed large language models' (LLMs) accuracy, surpassing traditional methods like SVD.
NVIDIA Enhances Long-Context LLM Training with NeMo Framework Innovations
NVIDIA's NeMo Framework introduces efficient techniques for long-context LLM training, addressing memory challenges and optimizing performance for models processing millions of tokens.
NVIDIA Unveils Advanced Optimization Techniques for LLM Training on Grace Hopper
NVIDIA introduces advanced strategies for optimizing large language model (LLM) training on the Grace Hopper Superchip, enhancing GPU memory management and computational efficiency.
NVIDIA Grace Hopper Revolutionizes LLM Training with Advanced Profiling
Explore how NVIDIA's Grace Hopper architecture and Nsight Systems optimize large language model (LLM) training, addressing computational challenges and maximizing efficiency.
Exploring LLM Agents and Their Role in AI Reasoning and Test Time Scaling
Discover the impact of large language model (LLM) agents on AI reasoning and test time scaling, highlighting their use in workflows and chatbots, according to NVIDIA.
Together Introduces Code Interpreter API for Seamless LLM Code Execution
Together.ai launches the Together Code Interpreter (TCI), an API enabling developers to execute LLM-generated code securely and efficiently, enhancing agentic workflows and reinforcement learning operations.
NVIDIA Unveils Nemotron-CC: A Trillion-Token Dataset for Enhanced LLM Training
NVIDIA introduces Nemotron-CC, a trillion-token dataset for large language models, integrated with NeMo Curator. This innovative pipeline optimizes data quality and quantity for superior AI model training.