Qwen 3 30B-A3B vs Qwen 3 VL 30B-A3B

The two models at a glance

About Qwen 3 30B-A3B

Alibaba's Qwen 3 MoE with 30B total and just 3B active parameters, supporting hybrid thinking mode. MMLU 81.4, AIME24 80.4, 100+ languages, Apache 2.0. Strengths: 3B active parameters keeps inference fast and cheap, MMLU 81.4 and AIME24 80.4 — strong on both general and reasoning, Apache 2.0, Hybrid thinking toggle per request.

About Qwen 3 VL 30B-A3B

Qwen 3 VL's sweet spot: a 30B MoE with 3B active parameters and 256k context. Delivers most of the 235B's quality at a fraction of the hardware cost. Strengths: Around 19 GB VRAM at Q4 — fits a single 24 GB card, Native 262k multimodal context, Efficient MoE with only 3B active parameters, Apache 2.0.

How they compare

Qwen 3 30B-A3B comes from Alibaba and Qwen 3 VL 30B-A3B from Alibaba. This comparison is built entirely from structured specs — parameter count, VRAM by quantization, context window, license, and published benchmark scores — so the verdict below reflects measurable differences rather than marketing claims.

Qwen 3 30B-A3B and Qwen 3 VL 30B-A3B share the same 30B parameter class. Both need about 19 GB of VRAM at a Q4 quantization, so they fit the same GPU tier.

The two models target different sweet spots: Qwen 3 30B-A3B is tuned for chat, general, reasoning, multilingual, moe, while Qwen 3 VL 30B-A3B leans toward vision, chat, general, moe, multilingual. Match the model to your dominant workload rather than to raw size.

For long-context work, Qwen 3 VL 30B-A3B offers the bigger window (256k vs 128k tokens).

Memory, quantization & throughput

Across quantization levels, Qwen 3 30B-A3B requires Q4 ≈ 19 GB, Q5 ≈ 23 GB, Q8 ≈ 35 GB, FP16 ≈ 62 GB, while Qwen 3 VL 30B-A3B requires Q4 ≈ 19 GB, Q5 ≈ 23 GB, Q8 ≈ 35 GB, FP16 ≈ 62 GB. In practice Qwen 3 30B-A3B wants a 24 GB card at Q4, so plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity of Q8 or FP16.

Without a GPU, Qwen 3 30B-A3B needs roughly 32 GB of system RAM to run on CPU and Qwen 3 VL 30B-A3B about 32 GB — workable for offline use but far slower than GPU inference. On a mid-range GPU you can expect on the order of 40 tokens/sec from Qwen 3 30B-A3B and 40 from Qwen 3 VL 30B-A3B, scaling up to 100 and 100 tokens/sec on high-end hardware.

Which fits your GPU

Here is the highest-quality quantization of each model that fits common GPU memory budgets, so you can match Qwen 3 30B-A3B or Qwen 3 VL 30B-A3B to the card you actually own:

• On a 24 GB GPU: Qwen 3 30B-A3B runs at Q5 (23 GB); Qwen 3 VL 30B-A3B runs at Q5 (23 GB).

Benchmark scores

Reported benchmarks for Qwen 3 30B-A3B: MMLU (base) 81.38, AIME 2024 80.4.

Bottom line: which should you pick?

• Pick Qwen 3 VL 30B-A3B for long-context work (up to 256k tokens).
• Pick Qwen 3 30B-A3B if your workload is reasoning.
• Pick Qwen 3 VL 30B-A3B if your workload is vision.

Which GPU should you buy to run Qwen 3 30B-A3B?

To run Qwen 3 30B-A3B locally at Q4, you need ~19 GB of VRAM. The best value for this is a RTX 4090 (24 GB VRAM).

Frequently asked questions

What is the difference between Qwen 3 30B-A3B and Qwen 3 VL 30B-A3B?

The headline differences: both are 30B models; their context windows differ (128k vs 256k tokens). Below we break down VRAM by quantization, benchmark scores, and a use-case verdict so you can pick the right one.

Can Qwen 3 30B-A3B and Qwen 3 VL 30B-A3B run on a 24 GB GPU?

At a Q4 quantization, Qwen 3 30B-A3B needs about 19 GB of VRAM and fits comfortably on a 24 GB GPU; Qwen 3 VL 30B-A3B needs about 19 GB and fits comfortably on a 24 GB GPU. Both have the same Q4 footprint.

What licenses do Qwen 3 30B-A3B and Qwen 3 VL 30B-A3B use?

Qwen 3 30B-A3B is licensed under Apache 2.0 and Qwen 3 VL 30B-A3B under Apache 2.0.

Which has the longer context window, Qwen 3 30B-A3B or Qwen 3 VL 30B-A3B?

Qwen 3 VL 30B-A3B has the larger context window (256k vs 128k tokens), so it handles longer documents and codebases in a single prompt.