Provides a platform for deploying and optimizing generative AI models, including large language models (LLMs), with tools for fine-tuning, real-time monitoring, and autoscaling. Reduces GPU costs by over 50% and improves inference performance with techniques like iteration batching, native quantization, and dedicated GPU resource management, enabling businesses to scale AI applications efficiently and securely.
Funding
$6.8M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Serving generative AI models, particularly large language models (LLMs), can be computationally expensive, leading to high GPU costs and slow inference speeds. Existing solutions often lack the optimization techniques needed to efficiently deploy and scale these models in production environments.
Solution
FriendliAI provides a comprehensive platform for optimizing and deploying generative AI models, enabling businesses to achieve faster inference speeds and reduce GPU costs. The platform offers tools for fine-tuning, real-time monitoring, and autoscaling, along with advanced optimization techniques like iteration batching, native quantization, and a dedicated DNN library. FriendliAI's engine supports a wide array of quantization techniques, including FP8, INT8, and AWQ, and is compatible with both open-source and custom LLMs. The platform's capabilities extend to building and serving compound AI systems for complex tasks, with features like model-agnostic function calls, structured outputs, and seamless data integration for real-time retrieval-augmented generation (RAG).
Target Audience
FriendliAI targets businesses and developers who need to deploy and scale generative AI applications efficiently, including those working with LLMs, multimodal models, and AI agents.
Features
- Support for a wide array of quantization techniques, including FP8, INT8, and AWQ
- Optimized GPU kernels for generative AI through the Friendli DNN Library
- Intelligent caching of computational results with Friendli TCache
- Model-agnostic function calls and structured outputs for reliable API integrations
- Seamless data integration for real-time RAG, reducing hallucinations
- Integration with Weights & Biases Registry and Hugging Face Model Hub for model deployment
- Multi-LoRA serving for efficient fine-tuning and deployment
- Autoscaling capabilities to adjust resources based on real-time demand
- Dedicated GPU resource management for consistent access to computing resources