8 Integrations with LFM3

View a list of LFM3 integrations and software that integrates with LFM3 below. Compare the best LFM3 integrations as well as features, ratings, user reviews, and pricing of software that integrates with LFM3. Here are the current LFM3 integrations in 2026:

  • 1
    OpenRouter

    OpenRouter

    OpenRouter

    OpenRouter is an AI model routing platform that gives developers access to hundreds of models through a single unified API. It connects users with models from providers such as OpenAI, Anthropic, Google, Meta, Mistral, DeepSeek, Qwen, xAI, and many others. The platform supports text, image, video, and audio generation while allowing developers to use one API key and a consistent interface across providers. OpenRouter can route requests based on price, performance, and availability, with fallback options that help maintain service when a provider experiences downtime. It also offers configurable data policies so organizations can control which providers receive prompts and how requests are handled. Developers can purchase credits, choose from more than 500 active models across over 80 providers, and integrate OpenRouter using an OpenAI-compatible API.
    Starting Price: Free
  • 2
    Hugging Face

    Hugging Face

    Hugging Face

    Hugging Face is a leading platform for AI and machine learning, offering a vast hub for models, datasets, and tools for natural language processing (NLP) and beyond. The platform supports a wide range of applications, from text, image, and audio to 3D data analysis. Hugging Face fosters collaboration among researchers, developers, and companies by providing open-source tools like Transformers, Diffusers, and Tokenizers. It enables users to build, share, and access pre-trained models, accelerating AI development for a variety of industries.
    Starting Price: $9 per month
  • 3
    Ollama

    Ollama

    Ollama

    Ollama is an innovative platform that focuses on providing AI-powered tools and services, designed to make it easier for users to interact with and build AI-driven applications. Run AI models locally. By offering a range of solutions, including natural language processing models and customizable AI features, Ollama empowers developers, businesses, and organizations to integrate advanced machine learning technologies into their workflows. With an emphasis on usability and accessibility, Ollama strives to simplify the process of working with AI, making it an appealing option for those looking to harness the potential of artificial intelligence in their projects.
    Starting Price: Free
  • 4
    Vercel AI Gateway
    Vercel AI Gateway is a unified AI infrastructure platform that allows developers to access, manage, and route requests across hundreds of AI models and providers through a single API interface. Built as part of the Vercel AI ecosystem, the platform supports text, image, and video generation models from providers such as OpenAI, Anthropic, SpaceXSI, and others while simplifying authentication, billing, observability, and failover management. Developers can use one API key and centralized dashboard to integrate multiple AI providers into applications without managing separate provider accounts or infrastructure. The platform also includes built-in routing, automatic failovers, usage tracking, unified billing, and compatibility with SDKs such as the Vercel AI SDK, enabling faster development and more resilient AI-powered applications.
  • 5
    Unsloth

    Unsloth

    Unsloth

    Unsloth is an open source platform designed to accelerate and optimize the fine-tuning and training of Large Language Models (LLMs). It enables users to train custom models, such as ChatGPT, in just 24 hours instead of the typical 30 days, achieving speeds up to 30 times faster than Flash Attention 2 (FA2) while using 90% less memory. Unsloth supports both LoRA and QLoRA fine-tuning techniques, allowing for efficient customization of models like Mistral, Gemma, and Llama versions 1, 2, and 3. Unsloth's efficiency stems from manually deriving computationally intensive mathematical steps and handwriting GPU kernels, resulting in significant performance gains without requiring hardware modifications. Unsloth delivers a 10x speed increase on a single GPU and up to 32x on multi-GPU systems compared to FA2, with compatibility across NVIDIA GPUs from Tesla T4 to H100, and portability to AMD and Intel GPUs.
    Starting Price: Free
  • 6
    Hugging Face Transformers
    ​Transformers is a library of pretrained natural language processing, computer vision, audio, and multimodal models for inference and training. Use Transformers to train models on your data, build inference applications, and generate text with large language models. Explore the Hugging Face Hub today to find a model and use Transformers to help you get started right away.​ Simple and optimized inference class for many machine learning tasks like text generation, image segmentation, automatic speech recognition, document question answering, and more. A comprehensive trainer that supports features such as mixed precision, torch.compile, and FlashAttention for training and distributed training for PyTorch models.​ Fast text generation with large language models and vision language models. Every model is implemented from only three main classes (configuration, model, and preprocessor) and can be quickly used for inference or training.
    Starting Price: $9 per month
  • 7
    LM Studio

    LM Studio

    LM Studio

    Use models through the in-app Chat UI or an OpenAI-compatible local server. Minimum requirements: M1/M2/M3 Mac, or a Windows PC with a processor that supports AVX2. Linux is available in beta. One of the main reasons for using a local LLM is privacy, and LM Studio is designed for that. Your data remains private and local to your machine. You can use LLMs you load within LM Studio via an API server running on localhost.
  • 8
    vLLM

    vLLM

    vLLM

    vLLM is a high-performance library designed to facilitate efficient inference and serving of Large Language Models (LLMs). Originally developed in the Sky Computing Lab at UC Berkeley, vLLM has evolved into a community-driven project with contributions from both academia and industry. It offers state-of-the-art serving throughput by efficiently managing attention key and value memory through its PagedAttention mechanism. It supports continuous batching of incoming requests and utilizes optimized CUDA kernels, including integration with FlashAttention and FlashInfer, to enhance model execution speed. Additionally, vLLM provides quantization support for GPTQ, AWQ, INT4, INT8, and FP8, as well as speculative decoding capabilities. Users benefit from seamless integration with popular Hugging Face models, support for various decoding algorithms such as parallel sampling and beam search, and compatibility with NVIDIA GPUs, AMD CPUs and GPUs, Intel CPUs, and more.
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