llama Tools

vLLM is a high-throughput and memory-efficient inference and serving engine for Large Language Models (LLMs). It solves the problem of expensive and inefficient LLM deployment by maximizing hardware utilization with features like PagedAttention and continuous batching, drastically reducing inference costs. It's designed for developers and organizations who need to deploy a wide range of open-source LLMs easily and affordably on any hardware, offering a drop-in OpenAI-compatible API for instant integration.

Higgsfield is a fault-tolerant, highly scalable GPU orchestration platform and machine learning framework. It solves the immense complexity of training and deploying models with billions to trillions of parameters across large, distributed clusters. It is designed for AI researchers, engineers, and organizations pushing the boundaries of large-scale model training who need robust, production-ready infrastructure.

YuE2: frontier music generation with symbolic planning, zero-shot covers, and agentic music editing.

Unsloth is an open-source desktop application that allows you to run and train large language models (LLMs) and diffusion models locally on your own hardware. It solves the problem of needing expensive cloud compute or specialized infrastructure to work with advanced AI models like Qwen3.8, Gemma 4, and FLUX. It's designed for developers, researchers, and AI enthusiasts who want full control, privacy, and cost-effectiveness in their AI projects.

AirLLM is a library that enables efficient inference for large language models like the 70B parameter class using just a single 4GB GPU. It solves the problem of high hardware requirements and costs typically associated with running state-of-the-art LLMs. This makes advanced AI capabilities accessible to developers, researchers, and small teams with limited computational resources.