Qwen3-235B-A22B
Qwen3-235B-A22B is a 235-billion-parameter mixture-of-experts (MoE) language model from Alibaba's Qwen team. It activates only 22B parameters per inference step, balancing capability with efficiency. The model uniquely supports toggling between 'thinking mode' (for complex reasoning) and 'non-thinking mode' (for fast dialogue) within a single model, and claims strong performance on math, coding, reasoning, and multilingual tasks.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | Qwen |
| Parameters | 235.1B |
| Context window | Unknown |
| License | apache-2.0 — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 918.8k |
| Likes | 1.1k |
| Last updated | 2025-07-26 |
| Source | Qwen/Qwen3-235B-A22B |
What Qwen3-235B-A22B is
MoE architecture with 128 experts (8 active per token), 94 layers, GQA attention (64 Q heads, 4 KV heads), native 32K context (extendable to 131K via YaRN). Causal language model trained with post-training optimization. Requires transformers >= 4.51.0. Supports thinking/non-thinking mode switching via `enable_thinking` flag and soft `/think`/`no_think` user directives. Compatible with vLLM, SGLang, Ollama, llama.cpp, and KTransformers.
Run Qwen3-235B-A22B locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="Qwen/Qwen3-235B-A22B")out = pipe("Explain retrieval-augmented generation in one sentence.", max_new_tokens=128)print(out[0]["generated_text"])Swap in vLLM or Ollama for production-grade serving. DEV.co can stand up the inference stack.
How you'd run it
A typical self-hosted path — open weights, an inference server, your application.
DEV.co builds each layer — from GPU infrastructure to the application.
Best use cases
Running & fine-tuning it
Estimated minimum 50–80GB VRAM for bfloat16 (no quantization) on multi-GPU setups (e.g., 8× H100/A100). Single-GPU inference requires quantization (AWQ, GPTQ, int8) to reduce footprint to ~30–50GB. Exact requirements depend on batch size and context length; consult deployment docs (vLLM, SGLang, TGI) for precision-specific guidance. MoE sparsity (22B active) reduces per-token compute vs. dense 235B models.
Card does not document LoRA, QLoRA, or fine-tuning procedures. Implications: (1) full fine-tuning likely requires multi-GPU distributed training; (2) parameter-efficient tuning (LoRA) feasibility is unknown. Recommend consulting Qwen GitHub/docs or community reports before committing to fine-tuning workflows. MoE architecture may complicate adapter placement.
When to avoid it — and what to weigh
- Strict latency SLAs without reasoning disabled — Thinking mode incurs significant overhead (generates reasoning tokens before response). Non-thinking mode mitigates this, but avoid if you need sub-100ms response times across all queries without model-level switching.
- Limited GPU memory (< 50GB VRAM) — Even with MoE sparsity, 235B parameters require substantial memory. Quantization (e.g., AWQ, GPTQ) or 8-bit inference is likely necessary. Single-GPU deployment is not feasible without aggressive quantization.
- Requirement for deterministic, greedy outputs — Model card explicitly warns: 'DO NOT use greedy decoding' in thinking mode; use temperature=0.6, top_p=0.95 instead. If determinism is critical, evaluate carefully or disable thinking mode (requires trade-off).
- Specialized domain fine-tuning with limited compute — No LoRA/QLoRA guidance in card. Full fine-tuning a 235B model requires multi-GPU clusters. Feasibility for domain adaptation on consumer hardware is unclear.
License & commercial use
Apache 2.0 license. Permissive OSI-compliant license allowing use, modification, and redistribution (including commercial use) under standard Apache 2.0 terms.
Apache 2.0 is a permissive, OSI-approved open-source license that explicitly permits commercial use. No gating, no restricted use restrictions in the license itself. However: (1) Verify downstream dependencies (e.g., transformers, vLLM) have compatible licenses; (2) review Qwen's terms of service on HuggingFace if using their inference endpoints; (3) ensure compliance with any third-party tokenizer or inference frameworks used in production. No proprietary model weights restrictions detected.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Strong |
| License clarity | Clear |
| Deployment complexity | Moderate |
| DEV.co fit | Strong |
| Assessment confidence | High |
Standard LLM security considerations apply: (1) Model outputs may hallucinate or reflect training-data biases; (2) thinking mode generates internal reasoning tokens—validate that sensitive intermediate logic is not exposed unintentionally; (3) no explicit security audit or adversarial robustness data provided; (4) when deployed via APIs (vLLM, SGLang), enforce standard API security (rate limiting, input validation, access control); (5) quantized or pruned variants may alter adversarial properties (requires empirical validation).
Alternatives to consider
Llama 3.1 405B
Dense 405B model with strong reasoning and multilingual support. Larger parameter count but no MoE sparsity. Higher inference cost, stronger benchmarks reported by Meta, but less efficient per-token compute.
DeepSeek-R1
Reasoning-focused model with chain-of-thought capabilities. Smaller dense variant available (e.g., 70B). Comparable reasoning features, different training approach, less established multilingual support.
Qwen2.5-72B or Qwen2.5-32B
Smaller dense alternatives from same Qwen family. Lower VRAM requirements, simpler deployment, but no thinking/non-thinking mode toggle and reduced reasoning capability. Faster inference without reasoning overhead.
Ship Qwen3-235B-A22B with senior software developers
Start with vLLM or SGLang for optimized serving. Estimate GPU requirements based on your batch size and thinking-mode usage. Review the Qwen GitHub and deployment docs for production best practices.
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Qwen3-235B-A22B FAQ
Can I use Qwen3-235B-A22B commercially?
What GPU setup do I need for production inference?
How do I disable thinking mode for faster responses?
Can I fine-tune this model for my domain?
Custom software development services
Need help beyond evaluating Qwen3-235B-A22B? DEV.co is a software development agency offering software development services and web development for teams of every size. Our software developers and web developers build custom software, web applications, APIs, and open-source llms integrations — and maintain them long-term.
Ready to Deploy Qwen3-235B-A22B?
Start with vLLM or SGLang for optimized serving. Estimate GPU requirements based on your batch size and thinking-mode usage. Review the Qwen GitHub and deployment docs for production best practices.