Qwen3 30B A3B Alternatives

Qwen3-235B-A22B-FP8 is a very large Mixture-of-Experts language model from Alibaba's Qwen team. With 235 billion total parameters and 22 billion activated per token, it supports advanced tool calling, multi-step reasoning, and complex instruction following. The FP8 version enables more efficient inference for this frontier-scale model.

Qwen3-30B-A3B-Instruct-2507-FP8 is an FP8 quantized variant of Alibaba's Qwen3 30B-A3B Mixture-of-Experts instruct model. It supports advanced features such as tool calling and is optimized for efficient inference. The model is hosted on Hugging Face and is suitable for developers needing high-performance language capabilities with lower memory footprint.

A 4 billion parameter version of the Qwen3 large language model in FP8 precision. It supports instruction following, tool calling, and general text generation. The model is hosted on Hugging Face and can be used locally or via inference providers.

Qwen3-32B-FP8 is an FP8-quantized version of Alibaba's Qwen3 32B large language model. It supports advanced capabilities including tool calling, long-context understanding, and multilingual performance while significantly reducing VRAM usage compared to the original. The model is designed for local and self-hosted inference by developers building AI applications.

Qwen3-30B-A3B-GPTQ-Int4 is a GPTQ 4-bit quantized variant of the Qwen3 30B-A3B model. It supports advanced capabilities such as tool calling and follows a detailed chat template for conversational use. The model is optimized for local or server-based inference using tools like llama.cpp or Hugging Face Text Generation Inference while maintaining strong performance.

Qwen3-1.7B-FP8 is a quantized variant of Alibaba's Qwen3 series of large language models. It supports advanced features including tool calling and follows a chat template optimized for instruction following. The FP8 format enables faster inference with reduced memory requirements while maintaining strong performance.

Qwen3-235B-A22B is a 235 billion parameter Mixture-of-Experts model with 22 billion active parameters, provided in FP8 precision. It is an instruction-tuned model supporting advanced features such as tool calling. The model represents the latest generation of the Qwen series and is available for local and hosted inference.

Qwen3-30B-A3B-GGUF provides GGUF quantized weights for the Qwen3 30B-A3B model, enabling efficient local execution with tools like llama.cpp. It supports advanced features including tool calling and follows a specific chat template for multi-turn conversations. The model is intended for developers who want to run powerful language models offline.

Qwen3-14B-FP8 is a quantized version of Alibaba's Qwen3 14B model optimized for efficient inference. It supports advanced features such as tool calling and follows an instruction-tuned chat template. The model is distributed on Hugging Face for developers who need high-performance open language models that can run on consumer or enterprise hardware.

Qwen3.6-27B-FP8 is a community-quantized variant of Alibaba's Qwen3 model offered in FP8 precision for lower memory usage while retaining strong performance. It supports text and vision inputs and is compatible with the Hugging Face ecosystem for local or cloud deployment. It is intended for developers building efficient multimodal applications.

This NVIDIA-hosted quantized version of the Qwen 3.5 397B (with 17B active parameters) model supports vision and video inputs in addition to text. It includes advanced prompting templates for tool use and multimodal content. The model is designed for high-performance inference using NVIDIA-optimized stacks.

Qwen/Qwen3.6-35B-A3B-FP8 is an open-source large language model designed for advanced text generation and understanding. It supports instruction following and multilingual capabilities, making it suitable for developers and researchers building AI-powered solutions.

Qwen3-30B-A3B-NVFP4 is an NVIDIA-optimized version of the Qwen3 model using NVFP4 quantization. It supports advanced tool calling and function calling through a detailed chat template. The model is designed for efficient inference on NVIDIA GPUs while preserving the capabilities of the original Qwen3 architecture.

Qwen3-8B-GGUF is the GGUF-quantized format of Alibaba's Qwen3-8B model. This version enables efficient local inference on consumer hardware using tools like llama.cpp or Ollama. It retains strong language understanding and generation capabilities while supporting tool calling and structured prompting formats.

Qwen3.6-27B-NVFP4 is an FP4 quantized variant of the Qwen3.6-27B model. It supports both text and vision inputs including images and video through specialized tokens and templates. The model is designed for efficient local or hosted inference using the Transformers library and is available on the Hugging Face model hub.

Qwen3-VL-235B-A22B-Instruct-FP8 is a massive multimodal model from the Qwen3 family, combining 235B and 22B parameters in a Mixture-of-Experts architecture. It is instruction-tuned for vision-language tasks and provided in an FP8 quantized format for more efficient inference. The model supports advanced tool use and multimodal understanding.

This is an AWQ-quantized 27B parameter version of Alibaba's Qwen3.5 language model. It supports text generation, tool use, and multimodal inputs while requiring significantly less memory than the original. The model is distributed via Hugging Face for use with popular inference frameworks.

Qwen3-0.6B-FP8 is an open-source, compact language model for text generation and tool calling. It is suitable for developers and researchers seeking a lightweight LLM for integration and experimentation.

Qwen3.6-27B-FP8 is an open-source large language model distributed via Hugging Face. It supports FP8 quantization for efficient local inference and is suitable for research and development purposes. The model is accessible to AI researchers and developers.

This is the GGUF quantized format of Alibaba's Qwen3-4B large language model. It enables efficient local inference using tools like llama.cpp or LM Studio. The model includes support for tool calling and follows a specific chat template for structured interactions.

Qwen3-32B-NVFP4 is an FP4 quantized version of the Qwen3-32B model created by NVIDIA. It includes support for tool calling and is optimized for high-performance inference on NVIDIA GPUs. The model is distributed on Hugging Face for developers seeking state-of-the-art performance with reduced memory and compute requirements.

nvidia/Qwen3-8B-NVFP4 provides a quantized version of the Qwen3 8B model optimized for NVIDIA GPUs. It supports advanced features such as tool calling and is designed for high-performance local or server-side inference. The model is distributed via Hugging Face.

This is a highly optimized, quantized version of the Qwen 3.6 35B model using NVFP4 precision for accelerated inference. It supports both text and vision inputs while maintaining strong performance. Distributed via Hugging Face, it is designed for developers needing fast multimodal inference on consumer or enterprise GPUs with Unsloth and Transformers compatibility.

Qwen3.6-35B-A3B-AWQ-4bit is a quantized version of a large language model, designed for efficient inference on local hardware. It is suitable for developers and researchers who need high-performance language models with reduced resource requirements.

This repository provides GGUF quantized files for the Qwen3.6-35B model using an A3B (likely MoE or distilled) architecture. It is optimized for local inference with tools such as LM Studio. The model includes advanced tool-calling capabilities and a sophisticated chat template for complex interactions.

This is an INT4 GPTQ-quantized version of the Qwen3.5-27B model from the Qwen team. It supports multimodal inputs including text, images, and video. The quantization enables more efficient inference while maintaining strong performance across various tasks.

Qwen3-4B-Thinking-2507-FP8 is a quantized release of Alibaba's Qwen3 thinking model optimized for local execution. It features advanced reasoning capabilities and multi-step tool calling support. The FP8 format significantly reduces memory usage while maintaining strong performance for complex problem solving and agentic workflows.

This is an AWQ-quantized 4B parameter version of the Qwen 3.5 model optimized for efficient inference. It supports advanced features including tool use and vision capabilities. The model is distributed on Hugging Face for use with Transformers or local inference engines.

Qwen3-30B-A3B-Base is a Mixture-of-Experts base model released by Qwen. It is distributed as open weights on Hugging Face and supports advanced prompting, tool use, and efficient inference through quantization. It can be installed via pip or Docker and deployed on local hardware or cloud platforms.

This is a GGUF-quantized version of the Qwen3.6-35B-A3B model, optimized for efficient local inference using tools like llama.cpp. It supports multimodal inputs and is designed for developers who want to run powerful language models on standard hardware without relying on cloud APIs.

This repository contains a w8a8 quantized version of the Qwen3.5-9B model from RedHatAI. It supports both text and vision inputs and is optimized for lower memory usage and faster inference. The model includes vision tower components and is compatible with the Transformers library.

This is a quantized (NVFP4) variant of the Qwen3.5-122B model optimized by NVIDIA for efficient inference. It supports advanced features including tool calling, multimodal inputs, and is designed to run on NVIDIA GPUs with significantly lower memory requirements than the original model. The model is distributed on Hugging Face and can be used with standard Transformers pipelines or custom inference servers.

This is a community-quantized 8-bit version of Qwen3.6-35B (with A3B MoE architecture) prepared for the MLX framework on Apple devices. It enables high-performance local inference on Macs with reduced memory footprint while retaining strong reasoning capabilities. The model uses standard MLX conversion and loading patterns.

A 4-bit AWQ quantized checkpoint of the Qwen3.5-35B-A3B Mixture-of-Experts model. It supports multimodal inputs including images and video and is optimized for local execution using tools compatible with the GGUF or AWQ format. The model is intended for developers wanting high-performance local AI capabilities.

Qwen3-14B-GGUF contains GGUF format files for the 14 billion parameter Qwen3 model, enabling efficient local execution using tools like llama.cpp. It supports advanced features such as tool calling and follows a specific chat template for multi-turn conversations. This allows developers to run a powerful open LLM on consumer hardware without relying on cloud APIs.