FriendliAI vs ggml.ai

FriendliAI is dedicated to advancing the capabilities of generative AI by providing efficient and cost-effective infrastructure solutions that cater to a wide array of needs. With groundbreaking performance on offer, FriendliAI boasts being 7.5 times cheaper than OpenAI GPT-3.5, achieving 16 times higher throughput, and delivering 24 times lower latency. These remarkable metrics open new frontiers for businesses seeking to integrate generative AI models into their operations.

The company offers three core services to run generative AI models with its Friendli Engine: Friendli Container, Friendli Dedicated Endpoints, and Friendli Serverless Endpoints. Each of these is designed to meet different operational needs—whether it's serving large language models (LLMs) in private environments, building and serving custom LLMs on autopilot, or accessing fast and affordable APIs for open-source LLMs and LMMs.

Customer success stories on the FriendliAI website highlight how various companies have reduced their costs and operational burdens. For instance, NaCloud's novel writing service and Scatter Lab's chatbot have both halved their costs, while TUNiB has enjoyed a more convenient and reliable chatbot service without added management efforts.

FriendliAI also announces the integration of its Friendli Engine with Amazon Sagemaker Jumpstart, enabling users to better serve JumpStart Foundation Models with enhanced performance and cost efficiency.

The company's strong commitment to generative AI infrastructure is further underpinned by their contact information, showing presence in both Redwood City, California, and Seoul, Korea, encouraging potential clients to get started on harnessing the power of their AI models.

ggml.ai is at the forefront of AI technology, bringing powerful machine learning capabilities directly to the edge with its innovative tensor library. Built for large model support and high performance on common hardware platforms, ggml.ai enables developers to implement advanced AI algorithms without the need for specialized equipment. The platform, written in the efficient C programming language, offers 16-bit float and integer quantization support, along with automatic differentiation and various built-in optimization algorithms like ADAM and L-BFGS. It boasts optimized performance for Apple Silicon and leverages AVX/AVX2 intrinsics on x86 architectures. Web-based applications can also exploit its capabilities via WebAssembly and WASM SIMD support. With its zero runtime memory allocations and absence of third-party dependencies, ggml.ai presents a minimal and efficient solution for on-device inference.

Projects like whisper.cpp and llama.cpp demonstrate the high-performance inference capabilities of ggml.ai, with whisper.cpp providing speech-to-text solutions and llama.cpp focusing on efficient inference of Meta's LLaMA large language model. Moreover, the company welcomes contributions to its codebase and supports an open-core development model through the MIT license. As ggml.ai continues to expand, it seeks talented full-time developers with a shared vision for on-device inference to join their team.

Designed to push the envelope of AI at the edge, ggml.ai is a testament to the spirit of play and innovation in the AI community.