LLM

Quick comparison why Nvidia is preferred over its (only) competitor AMD for LLM work. Aspect NVIDIA GPUs AMD Radeon Pro Framework Support Extensive support via CUDA for PyTorch, TensorFlow, and most ML frameworks Limited support, fewer frameworks optimized for AMD ML Ecosystem Mature CUDA ecosystem with wide adoption Less developed ROCm ecosystem with limited compatibility Software Integration Well-established pipelines and tools More restricted options, may require additional setup Raw Computing Power Strong performance with direct ML optimization Good raw power but harder to leverage for ML tasks Memory Options Various models with sufficient VRAM (16GB+) Competitive VRAM options but harder to utilize for ML Primary Use Case Strong in both ML/AI and professional graphics Better suited for professional graphics work LLM Specific Support De facto standard for LLM deployment Limited practical application for LLMs Towards end of CY24: ...

As more developers and enthusiasts venture into running Large Language Models (LLMs) locally, one question keeps coming up: Which GPU should you choose? In this post, we’ll compare three popular NVIDIA options: the RTX 4070, 3090, and 4090, breaking down the technical jargon into practical terms. Understanding the Key Terms Before diving into the comparison, let’s decode what these specifications mean in real-world usage: VRAM (Video RAM) Think of VRAM as your GPU’s short-term memory: ...

When it comes to large language models, Mistral and Llama2 are two notable entries in the field, each with its unique attributes: Model Architecture Mistral: Known for its innovative approach, Mistral uses a sparse mixture-of-experts architecture, which allows for more efficient computation by activating only a subset of the model’s parameters for any given input. This leads to faster inference times and potentially lower computational costs. Llama2: Developed by Meta AI, Llama2 follows a more traditional transformer architecture but with significant optimizations for performance and efficiency. It focuses on scaling up the model size to improve capabilities. ...