What is FriendliAI?

FriendliAI offers a comprehensive solution designed to significantly accelerate generative AI inference, delivering substantial cost savings and enhanced performance for production environments. The platform leverages cutting-edge technologies such as Iteration Batching (also known as Continuous Batching), an optimized Friendli DNN Library, Friendli TCache for reusing computational results, and Native Quantization supporting FP8, INT8, and AWQ. This allows businesses to deploy and serve popular open-source models from Hugging Face Hub or their own custom Large Language Models (LLMs) and Large Multimodal Models (LMMs) with remarkable speed and efficiency, requiring fewer GPUs and achieving higher throughput with lower latency.

Beyond raw inference speed, FriendliAI provides an all-in-one platform for building and serving sophisticated AI agents. Users can effortlessly deploy custom models by uploading them directly or importing from W&B Registry or Hugging Face Model Hub. The system supports efficient model fine-tuning using Parameter-Efficient Fine-Tuning (PEFT) and Multi-LoRA serving. Advanced monitoring and debugging tools help optimize LLM performance, while model-agnostic function calls and structured outputs ensure reliable API integrations. Furthermore, FriendliAI facilitates real-time Retrieval-Augmented Generation (RAG) and allows integration with a library of predefined tools or custom-built ones, making it a versatile solution for complex AI tasks and ensuring models are enhanced with up-to-date information.

Features

Iteration Batching (Continuous Batching): Groundbreaking optimization technique for significantly improved inference throughput.
Friendli DNN Library: Optimized GPU kernels specifically designed for generative AI workloads.
Friendli TCache: Intelligently reuses computational results to reduce latency and improve efficiency.
Native Quantization: Supports FP8, INT8, and AWQ for efficient model serving without compromising accuracy.
Custom Model Deployment: Easily deploy models by uploading or importing from W&B Registry or Hugging Face Model Hub.
PEFT & Multi-LoRA Fine-tuning: Efficiently fine-tune models using Parameter-Efficient Fine-Tuning and deploy with Multi-LoRA serving.
Advanced LLM Monitoring: Tools for monitoring, debugging, and optimizing LLM performance.
Model-Agnostic Function Calls & Structured Outputs: Build reliable API integrations for AI agents, ensuring consistent results across models.
Real-Time RAG System: Enhances AI models with up-to-date information through Retrieval-Augmented Generation.
Flexible Tool Integration: Utilize a library of predefined tools or integrate custom tools to expand AI agent capabilities.
Intelligent Autoscaling: Automatically adjusts resources based on demand for optimal performance and cost efficiency, with scale-to-zero capability.

Use Cases

Accelerating inference for open-source and custom Large Language Models (LLMs) in production.
Reducing GPU costs and requirements for serving generative AI models.
Fine-tuning models with proprietary datasets for specialized enterprise applications.
Building and deploying complex AI agents with capabilities like function calling and RAG.
Powering high-traffic applications like personalized character chatbots requiring efficient token processing.
Operating large-scale LLM services for industries such as telecommunications with reliability and cost-efficiency.
Developing applications requiring long context understanding up to 128K tokens.

FAQs

What types of AI models can I deploy and fine-tune with FriendliAI?

FriendliAI supports a wide range of models, including popular open-source Large Language Models (LLMs) from Hugging Face Hub, custom LLMs, and Large Multimodal Models (LMMs). You can also fine-tune these models using your proprietary datasets with methods like PEFT.
How does FriendliAI help reduce the cost of running generative AI models?

FriendliAI achieves significant cost savings (50-90%) and requires fewer GPUs by using advanced optimization technologies such as Iteration Batching, optimized GPU kernels (Friendli DNN Library), intelligent caching (Friendli TCache), and native quantization (FP8, INT8, AWQ).
Can I deploy models in my own secure infrastructure using FriendliAI?

Yes, FriendliAI offers Friendli Container, which allows you to serve generative AI models within your secure environment, either on-premise or in a managed Kubernetes cluster, ensuring data privacy and control.
Does FriendliAI support building AI agents with external knowledge and tools?

Yes, FriendliAI facilitates building AI agents with real-time Retrieval-Augmented Generation (RAG) to enhance knowledge and supports model-agnostic function calls and integration with predefined or custom tools.
What deployment options does FriendliAI offer?

FriendliAI provides flexible deployment options: Friendli Dedicated Endpoints for managed LLM/LMM serving in the cloud, Friendli Container for serving in your secure environment, and Friendli Serverless Endpoints for fast, pay-as-you-go inference.