Phind Launches Flagship AI Model Phind-405B and Instant Search to Deliver Faster, High-Quality Answers

Phind, the AI-powered search engine for developers, today announced the launch of its flagship AI model, Phind-405B, alongside Phind Instant, a model designed to deliver lightning-fast answers. These new models are aimed at improving both the quality and speed of responses for programming and general technical inquiries.

Phind-405B: Pushing Boundaries in Technical Search

Phind-405B is built on Meta’s Llama 3.1 architecture, using 405 billion parameters to deliver state-of-the-art performance in programming tasks. Trained on 256 H100 GPUs using FP8 mixed precision, Phind-405B allows for high memory efficiency without compromising on accuracy, offering a 40% reduction in memory usage compared to traditional training methods. This model also supports an impressive 128K token context, making it particularly adept at handling large-scale technical queries.

Phind-405B excels in real-world applications, including the development of web apps and the design of technical solutions. For instance, when tasked with creating a landing page for Paul Graham’s “Founder Mode,” the model performed multiple searches and produced various website options, showcasing its ability to blend creativity with technical precision.

Web app about ‘Founder Mode’ generated using Phind

Phind Instant: Addressing Latency Issues in AI Search

Recognizing that latency remains a key challenge in AI-powered search, Phind has introduced the Phind Instant model. Running on the Meta Llama 3.1 8B architecture, this model leverages Phind’s custom-built NVIDIA TensorRT-LLM inference server, offering speeds of up to 350 tokens per second. These improvements significantly reduce the time users spend waiting for results, bringing the search experience closer to the speed of traditional search engines like Google.

Phind Instant uses advanced FP8 precision and optimized CUDA kernels, ensuring not only speed but also maintaining high answer quality. This model is particularly suited for users who need quick information summaries without sacrificing depth or relevance.

Enhanced Search Infrastructure

To further reduce latency, Phind has implemented a predictive prefetching mechanism that begins retrieving web results before the user has finished typing. This innovation can cut up to 800ms from search times, allowing for nearly instantaneous feedback.

In addition, Phind has upgraded the embeddings used in its search pipeline, moving to a model that is 15 times larger than its predecessor. This upgrade enhances the relevance of the information presented while simultaneously improving performance through 16-way parallelism.

Broad Applications for Developers and Beyond

Phind’s new models are aimed at empowering developers to experiment and bring new ideas to life faster. Beyond coding, Phind is emerging as a versatile tool for answering a wide range of technical and curiosity-driven questions, solidifying its position as a leading AI-powered search engine. The company credits partnerships with Meta, NVIDIA, AWS, and others for its advancements.

As Phind continues to refine its models and develop new features, it is set to expand its influence within the developer community and beyond, offering ever-faster, higher-quality answers.