Qwen3-30B-A3B-GPTQ-Int4
Qwen3-30B-A3B-GPTQ-Int4 is a 30.5B-parameter mixture-of-experts (MoE) model from Qwen that activates only 3.3B parameters per forward pass. It supports dual modes: thinking mode for complex reasoning (math, coding) and non-thinking mode for fast dialogue. The model is 4-bit quantized (GPTQ), reducing memory footprint, and supports 100+ languages. It is distributed under Apache 2.0 license without gating, making it freely available for commercial use.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | Qwen |
| Parameters | 30.5B |
| Context window | Unknown |
| License | apache-2.0 — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 129.2k |
| Likes | 54 |
| Last updated | 2025-05-21 |
| Source | Qwen/Qwen3-30B-A3B-GPTQ-Int4 |
What Qwen3-30B-A3B-GPTQ-Int4 is
Qwen3-30B-A3B uses a 48-layer causal language model with 128 experts (8 activated per token), grouped query attention (32 Q heads, 4 KV heads), and native context of 32,768 tokens (extendable to 131,072 via YaRN). The GPTQ 4-bit quantization reduces model size while maintaining inference quality. Requires transformers ≥4.51.0 to avoid KeyError. Compatible with vLLM and SGLang for OpenAI-compatible API deployment with reasoning parser support.
Run Qwen3-30B-A3B-GPTQ-Int4 locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="Qwen/Qwen3-30B-A3B-GPTQ-Int4")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
ESTIMATE 8–16GB VRAM for inference with 4-bit GPTQ quantization at batch_size=1, depending on context length and thinking mode overhead. Full-precision would require ~60GB. GPU recommended (NVIDIA A100/H100 or equivalent). CPU inference feasible for non-thinking mode on high-end systems but slow. Exact VRAM and throughput not stated in card.
Card does not discuss LoRA, QLoRA, or supervised fine-tuning feasibility. MoE architecture and 4-bit quantization may complicate standard fine-tuning workflows. Requires review of community practices (SGLang/vLLM plugins or HuggingFace trainer adaptation). Fine-tuning guidance on thinking-mode behavior is absent.
When to avoid it — and what to weigh
- Real-Time, Ultra-Low-Latency Requirements — Thinking mode generates extended reasoning chains (token overhead unknown). Non-thinking mode is faster, but 30B still requires careful resource planning for <100ms SLA.
- Limited GPU Memory (<8GB VRAM) — Even with 4-bit quantization, 30B-parameter MoE requires substantial VRAM. Exact footprint depends on batch size and context length; expect ESTIMATE 8–16GB for reasonable throughput.
- Greedy Decoding Dependency — Thinking mode explicitly forbids greedy decoding; requires temperature=0.6, top_p=0.95. Applications expecting deterministic output with fixed sampling will degrade in thinking mode.
- Niche Domain Specialization — No fine-tuning examples or domain-specific evaluation data provided. Suitability for specialized verticals (medical, legal, scientific) is Unknown without benchmark results.
License & commercial use
Apache License 2.0 (Apache-2.0 identifier). Permissive, OSI-approved license allowing use, modification, and distribution with minimal restrictions (attribution required, no warranty).
Apache 2.0 is a permissive open-source license that explicitly permits commercial use, including redistribution and derivative products, provided license text and copyright notices are included. No gating, no paid licensing tier. Commercial deployment is permitted without additional agreements.
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 |
No explicit security audit, adversarial robustness testing, or vulnerability disclosure policy stated in card. 4-bit quantization may reduce model transparency for interpretability/auditing. Thinking-mode token chains (reasoning steps) are unencrypted in standard deployments—handle carefully if processing sensitive data. Review Qwen's security advisories and use vetted serving frameworks (vLLM, SGLang) with access controls in production.
Alternatives to consider
Qwen2.5-32B-Instruct
Larger dense model without MoE; potentially simpler serving and fine-tuning but higher memory cost. No thinking mode. Baseline for comparison on instruction-following.
Llama 3.1-70B (quantized)
Larger dense model with mature ecosystem (vLLM, llama.cpp, Ollama). Strong reasoning and coding but no native thinking-mode toggle. Different license (Llama Community License—requires review for commercial use).
DeepSeek-R1-Distill-Qwen-32B
Specialized reasoning distill combining DeepSeek reasoning with Qwen base. Smaller than full Qwen3-30B but optimized for thinking tasks. Consider if reasoning-only workload.
Ship Qwen3-30B-A3B-GPTQ-Int4 with senior software developers
Start with vLLM or SGLang for production serving. Benchmark thinking vs. non-thinking mode on your hardware and latency targets. Contact us to architect a private LLM or custom agentic application using Qwen3.
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Qwen3-30B-A3B-GPTQ-Int4 FAQ
Can I use this model commercially without paying for a license?
How much VRAM do I need to run Qwen3-30B-A3B-GPTQ-Int4?
What is the difference between thinking and non-thinking modes?
Is this model suitable for fine-tuning?
Work with a software development agency
DEV.co is a software development agency delivering custom software development services to companies building on open source. Our software developers and web developers design, integrate, and ship production systems — spanning web development, APIs, AI, data, and cloud. If Qwen3-30B-A3B-GPTQ-Int4 is part of your open-source llms roadmap, our team can implement, customize, migrate, and maintain it.
Ready to Deploy Qwen3-30B-A3B-GPTQ-Int4?
Start with vLLM or SGLang for production serving. Benchmark thinking vs. non-thinking mode on your hardware and latency targets. Contact us to architect a private LLM or custom agentic application using Qwen3.