Explain GPU usage for model training
View NSQL Scores for Models
Predict customer churn based on input details
Display leaderboard of language model evaluations
Explore GenAI model efficiency on ML.ENERGY leaderboard
Optimize and train foundation models using IBM's FMS
Load AI models and prepare your space
Export Hugging Face models to ONNX
Track, rank and evaluate open LLMs and chatbots
View and submit machine learning model evaluations
View and submit LLM benchmark evaluations
Browse and filter machine learning models by category and modality
View and submit LLM benchmark evaluations
LLM Conf talk is a specialized tool designed for model benchmarking, particularly focusing on the analysis and optimization of GPU usage during large language model (LLM) training. It provides detailed insights into hardware performance, helping users understand and improve resource utilization for better training efficiency.
• Real-time GPU monitoring: Track GPU usage, memory allocation, and performance metrics during training. • Benchmarking capabilities: Compare performance across different hardware configurations and models. • Resource optimization: Identify bottlenecks and optimize GPU usage for faster training cycles. • Compatible with multiple frameworks: Supports popular machine learning frameworks like TensorFlow and PyTorch. • Customizable reporting: Generate detailed reports to analyze training efficiency and hardware performance.
What models are supported by LLM Conf talk?
LLM Conf talk is designed to work with a wide range of large language models, including but not limited to GPT, BERT, and transformer-based architectures.
Can I use LLM Conf talk with multiple GPUs?
Yes, LLM Conf talk supports multi-GPU setups, allowing you to benchmark and optimize performance across distributed training environments.
Is LLM Conf talk compatible with all deep learning frameworks?
While it is optimized for TensorFlow and PyTorch, it may work with other frameworks depending on their compatibility with GPU monitoring tools. Contact support for specific framework queries.