Accelerating LLMs with llama.cpp on NVIDIA RTX Systems
Table of Contents
- Accelerated Performance of llama.cpp on NVIDIA RTX
- Implementation of CUDA Graphs to reduce overheads
- Ecosystem of Developers Building with llama.cpp
- Applications Accelerated with llama.cpp on the RTX Platform
- Backyard.ai
- Brave
- Opera
Accelerated Performance of llama.cpp on NVIDIA RTX
NVIDIA collaborates on optimizing llama.cpp for RTX GPUs by implementing CUDA Graphs to enhance performance and reduce overheads in kernel execution times for token generation. Developers can build and utilize llama.cpp with NVIDIA GPU optimizations by visiting llama.cpp/docs on GitHub.
Ecosystem of Developers Building with llama.cpp
Developers have a wide array of frameworks and abstractions on top of llama.cpp to accelerate application development. Additionally, there are pre-optimized models available for leveraging llama.cpp on RTX systems, further enhancing development efficiency.
Applications Accelerated with llama.cpp on the RTX Platform
1. Backyard.ai
Backyard.ai allows users to engage with virtual characters creatively in a private environment by leveraging llama.cpp for LLM acceleration on RTX systems.
2. Brave
Brave integrates Leo, a smart AI assistant, utilizing Ollama powered by llama.cpp for accelerated inference on NVIDIA RTX systems. Users can interact with local LLMs through this integration.
3. Opera
In Opera's browser AI, Aria, users can access summarized web page information, translations, text/image generation, and more with support for over 50 languages. Ollama with llama.cpp on NVIDIA RTX GPUs accelerates local inference support in Opera's AI engine.
NVIDIA remains dedicated to enhancing and contributing to open-source software on the RTX AI platform.