Qwen3-30B-A3B

Our Take

A strong 30B-parameter MoE language model from Alibaba. High composite score across our benchmark mix — worth shortlisting when raw quality matters more than VRAM budget.

Generated from this model’s benchmarks and ranking signals. Editor reviews refine it over time.

For the local developer, this architecture creates a unique hardware profile. While the Qwen3-30B-A3B VRAM requirements are dictated by the total 30B parameter count (as the entire model must reside in memory), the Qwen3-30B-A3B tokens per second are more reflective of a 3B parameter model. This decoupling of memory capacity from compute intensity is the model's primary technical advantage.

Key technical specifications include:

• Context Length: 131,072 tokens, supported by advanced RoPE (Rotary Positional Embeddings) scaling.
• Architecture: MoE with 3B active parameters.
• Training Data: Cutoff in 2024, incorporating a massive multilingual and code-heavy corpus.
• License: Apache 2.0, allowing for permissive commercial use and modification.

The 131k context window is a critical feature for local RAG (Retrieval-Augmented Generation) applications. It allows practitioners to ingest entire codebases or long technical documents without the aggressive "lost in the middle" phenomena seen in smaller context models.

Qwen3-30B-A3B is a general-purpose model with a heavy emphasis on technical reasoning. Unlike many smaller models that struggle with multi-step logic, this model performs exceptionally well in the following areas:

Qwen3-30B-A3B for coding is one of the most common use cases for this model. It supports over 30 programming languages and excels at boilerplate generation, refactoring, and identifying logical errors in complex scripts. Because it was trained on a 2024 dataset, it has a better grasp of modern frameworks and API versions than older 30B-class models.

The model is fine-tuned for high-reliability tool use. In local agent setups, it can accurately format JSON and decide when to call external functions. Its ability to follow system prompts strictly makes it a reliable "brain" for local automation tasks where a 7B or 8B model might hallucinate the tool syntax.

Alibaba’s Qwen series consistently leads in multilingual benchmarks. Qwen3-30B-A3B handles non-English languages—particularly CJK (Chinese, Japanese, Korean) and European languages—with a level of nuance usually reserved for much larger models. On the Qwen3-30B-A3B reasoning benchmark, it shows strong performance in mathematical problem solving, making it suitable for local data analysis and STEM-related tutoring applications.

To run Qwen3-30B-A3B locally, your primary bottleneck will be VRAM. Because MoE models require all experts to be loaded into memory to prevent massive latency hits from disk swapping, you must have enough VRAM to hold the 30B parameters.

• Minimum VRAM (4-bit quantization): ~18GB - 20GB.
• Recommended VRAM (6-bit or 8-bit): 24GB - 32GB+.
• System RAM: If offloading to CPU (not recommended for MoE), at least 64GB.

The best GPU for Qwen3-30B-A3B is an NVIDIA RTX 3090 or 4090 with 24GB of VRAM. These cards allow you to run the model at 4-bit or 5-bit quantization with enough headroom for a decent context buffer. For Mac users, an M2/M3/M4 Max or Ultra with at least 32GB of Unified Memory provides a seamless experience.

The best quantization for Qwen3-30B-A3B for most practitioners is Q4_K_M (GGUF) or 4-bit (EXL2/AWQ).

• Q4_K_M: Offers the best balance between perplexity (intelligence) and memory footprint. It fits comfortably on a 24GB GPU.
• Q6_K: If you have 32GB+ of VRAM (e.g., dual 3060 12GBs or a Mac), the jump to 6-bit provides a measurable increase in reasoning accuracy for complex coding tasks.

On an RTX 4090 using the Q4_K_M quantization via Ollama or llama.cpp, you can expect Qwen3-30B-A3B tokens per second in the range of 40-60 t/s. This is significantly faster than a dense 30B model, which would typically hover around 15-25 t/s on the same hardware.

If you are using a 16GB VRAM card (like an RTX 4080 or 4070 Ti Super), you will need to use a lower quantization like IQ3_M or Q3_K_L. While this will fit, you may notice a slight degradation in complex reasoning. For these cards, it is often better to use a smaller dense model or accept the speed penalty of partial CPU offloading.

The quickest way to get started is via Ollama. Simply run:

ollama run qwen3:30b-a3b

When deciding whether to deploy this model, it is helpful to look at Qwen3-30B-A3B vs Mistral Small and its dense sibling, Qwen3-32B.

Mistral Small is a highly optimized dense model. While Mistral Small often exhibits slightly better English-language creative writing, Qwen3-30B-A3B typically wins in coding and math benchmarks. Furthermore, the MoE architecture of the Qwen model allows for higher throughput on mid-range hardware compared to the dense Mistral Small.

The dense 32B model is essentially the "full weight" version of this intelligence class.

• Intelligence: The dense 32B model has a slight edge in very niche edge cases and deep nuances due to every parameter being active.
• Speed: Qwen3-30B-A3B is roughly 2x to 3x faster in generation speed because it only calculates 3B parameters per token.
• VRAM: Both require approximately the same VRAM (~18-20GB for 4-bit).

For local practitioners, the choice usually comes down to the "latency tax." If you are running an interactive chat or an agent that needs to make many quick decisions, Qwen3-30B-A3B is the superior choice. If you are performing offline batch processing where speed doesn't matter, the dense Qwen3-32B may offer slightly more stability in its outputs.

In the current landscape of local AI, Qwen3-30B-A3B stands out as a highly pragmatic middle ground. It provides the "large model" feel of high-parameter reasoning without the "large model" wait times, provided you have the 24GB of VRAM required to house its expert library.