Qwen3.6-35B-A3B-MLX-3bit is an open-source, quantized checkpoint of the Qwen3.6-35B language model, compatible with MLX for efficient local inference. Below are 14 foundation models & chat apps with similar functionality to Qwen3.6 35B A3B, matched by what each product actually does — not ranked or scored. Explore each to find the closest fit for your use case.
Qwen3.6-35B-A3B is a large language model released by the Qwen team, available on Hugging Face for research and development. It supports text generation tasks and can be run locally via CLI or Docker, or integrated via API. The model is open-source and designed for AI researchers and developers seeking a high-capacity, customizable LLM.
Qwen3.5-27B-MLX-4.5bit is an open-source, quantized large language model designed for efficient local text generation using the MLX framework. It supports local inference and is suitable for developers and researchers seeking to run LLMs on their own hardware.
Qwen3.6-27B-AWQ-6Bit is a quantized version of the Qwen3.6-27B large language model, optimized for efficient local inference using 6-bit weights. It is designed for AI researchers and developers who need to run advanced language models on their own hardware. The model is open source and available for download 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.
Qwen3.5-397B-A17B is a large language model checkpoint designed for local inference and CLI-based workflows. It enables developers and researchers to run advanced language models on their own hardware for experimentation and application development.
Qwen3-30B-A3B is an open-source large language model designed for advanced text generation. Distributed under the Apache 2.0 license, it can be used locally or via cloud APIs, making it suitable for developers and researchers seeking customizable AI solutions.
Qwen3.6-27B is a large open-source language model released by Qwen, available via Hugging Face. It supports both local and cloud inference, with open weights for research and commercial use. Developers can install it using pip or Docker and integrate it into their AI workflows.
Qwen3.6-35B-A3B-GGUF is an open-source, quantized large language model distributed in GGUF format for local inference and research. It enables developers and researchers to run advanced language models on their own hardware, supporting experimentation and customization. The model is freely available for use and modification.
Qwen3.5-35B-A3B-GGUF is an open-source large language model designed for local inference and research. It enables developers and researchers to run text generation tasks efficiently on their own hardware with open weights.
Qwen3.5-35B-A3B-GGUF is an open-source large language model distributed by Unsloth on Hugging Face. It supports local inference, multiple quantizations, and is designed for AI researchers and developers seeking to run LLMs on their own hardware.
Qwen3.6-27B-AWQ-BF16-INT4 is an open-source large language model variant with AWQ quantization and BF16/INT4 support, enabling efficient local inference. Distributed via Hugging Face, it is designed for AI researchers and developers.
qwen2.5-7b-numbers-merkel-s3 is an open-source checkpoint of the Qwen2.5 7B language model, designed for developers and researchers to use in building and fine-tuning AI applications. It supports text generation and conversational AI tasks, and is distributed via Hugging Face with open weights for flexible deployment.
Qwen3.5-9B-quantized.w4a16 is a quantized version of the Qwen3.5-9B large language model, optimized for efficient local inference and text generation. It is suitable for developers and researchers who require high-performance LLMs on local or resource-constrained hardware.
Qwen35B-Agent-R2-GGUF is an open-source checkpoint of the Qwen 35B language model, designed for advanced text generation and agent-based AI tasks. It supports local inference and customization, making it suitable for AI researchers and developers integrating large models into their workflows.
