Technical Specifications

System Requirements

VRAM requirements for different quantization methods and context sizes

Qwen3-235B-A22B

Qwen3-235B-A22B is a flagship Mixture-of-Experts (MoE) large language model developed by Alibaba Cloud, forming part of the Qwen3 series. Its primary purpose is to address high-performance computational linguistics tasks requiring advanced reasoning and comprehensive knowledge. This model is engineered for handling complex assignments such as sophisticated code generation, intricate mathematical problem-solving, and multi-step logical deduction. It is also designed to be highly effective in applications that necessitate processing of extended documents, managing multi-turn conversations, and analyzing enterprise-scale datasets.

The technical architecture of Qwen3-235B-A22B incorporates a unified framework that integrates both a 'thinking mode' and a 'non-thinking mode'. The thinking mode facilitates complex, multi-step reasoning by explicitly showing intermediate thought processes, while the non-thinking mode provides rapid, direct responses. This dual-mode design enables dynamic switching based on task complexity or user queries, allowing for adaptive allocation of computational resources during inference. The MoE architecture is characterized by its sparse activation mechanism, utilizing top-2 expert routing, where each input token is dynamically routed to its two most relevant experts chosen from a total of 128 experts. Despite a total parameter count of 235 billion, only 22 billion parameters are actively engaged during inference for any given input, contributing to efficiency. The model's foundation is built upon a pre-training corpus of approximately 36 trillion tokens, encompassing 119 languages and dialects. Architectural components include Grouped-Query Attention (GQA) for optimized attention mechanisms, Rotary Positional Embedding (RoPE) for position encoding, and the integration of Flash Attention for accelerated processing. Normalization is performed using pre-norm RMSNorm, and the activation function employed is SwiGLU.

The performance characteristics of Qwen3-235B-A22B highlight its capabilities in instruction following, logical reasoning, comprehensive text understanding, and proficiency across mathematics, science, and coding tasks. Its design prioritizes efficiency, with the MoE architecture significantly lowering the computational resources required per inference step, thereby reducing energy consumption and operational costs. The model supports a substantial context length, which enhances its ability to maintain coherence and retrieve relevant information over long sequences. The weights are made publicly available under the Apache 2.0 license, promoting widespread adoption and further research within the artificial intelligence community. This accessibility allows for deployment across various frameworks and platforms, including local development environments such as Ollama, LMStudio, and llama.cpp.