Qwen 3 VL 30B-A3B vs LLaDA 2.0 Uni 16B
Side-by-side specs, benchmarks, and a verdict by use case.
Updated 2026-07-13
| Spec | Qwen 3 VL 30B-A3B | LLaDA 2.0 Uni 16B |
|---|---|---|
| Parameters | 30B | 16B |
| Author | Alibaba | Ant Group / inclusionAI |
| License | Apache 2.0 | Apache 2.0 |
| Context window | 0k | 0k |
| VRAM at Q4 | 19 GB | 18 GB |
| VRAM at Q5 | 23 GB | 22 GB |
| VRAM at Q8 | 35 GB | 30 GB |
| VRAM at FP16 | 62 GB | 47 GB |
| Use cases | vision, chat, general, moe, multilingual | chat, vision, general, moe |
Verdict
Qwen 3 VL 30B-A3B is significantly larger (30B vs 16B), so expect higher quality but heavier VRAM and slower throughput.
The two models at a glance
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.
About LLaDA 2.0 Uni 16B
Ant Group's first open Apache 2.0 diffusion LLM: a 16B/1B MoE paired with a 6.2B diffusion decoder, unifying text and vision generation and editing. Released April 2026. Strengths: The first Apache 2.0 open diffusion LLM, Unified text, vision, generation, and editing, Interleaved 'thinking' mode during diffusion, Decoder-turbo distillation runs 8 diffusion steps instead of 50.
How they compare
Qwen 3 VL 30B-A3B comes from Alibaba and LLaDA 2.0 Uni 16B from Ant Group / inclusionAI, they belong to the Qwen and LLaDA families respectively. 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.
At 30B vs 16B parameters, Qwen 3 VL 30B-A3B is the larger of the two. At Q4, LLaDA 2.0 Uni 16B fits in about 18 GB of VRAM versus 19 GB for the other — a 1 GB difference that matters on consumer GPUs.
The two models target different sweet spots: Qwen 3 VL 30B-A3B is tuned for vision, chat, general, moe, multilingual, while LLaDA 2.0 Uni 16B leans toward chat, vision, general, moe. Match the model to your dominant workload rather than to raw size.
On a typical mid-range GPU, LLaDA 2.0 Uni 16B pushes roughly 60 tokens/sec versus 40, so it is the more responsive choice for interactive or high-volume use. For long-context work, Qwen 3 VL 30B-A3B offers the bigger window (256k vs 8k tokens).
Memory, quantization & throughput
Across quantization levels, Qwen 3 VL 30B-A3B requires Q4 ≈ 19 GB, Q5 ≈ 23 GB, Q8 ≈ 35 GB, FP16 ≈ 62 GB, while LLaDA 2.0 Uni 16B requires Q4 ≈ 18 GB, Q5 ≈ 22 GB, Q8 ≈ 30 GB, FP16 ≈ 47 GB. In practice Qwen 3 VL 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 VL 30B-A3B needs roughly 32 GB of system RAM to run on CPU and LLaDA 2.0 Uni 16B about 36 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 VL 30B-A3B and 60 from LLaDA 2.0 Uni 16B, scaling up to 100 and 130 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 VL 30B-A3B or LLaDA 2.0 Uni 16B to the card you actually own:
- On a 24 GB GPU: Qwen 3 VL 30B-A3B runs at Q5 (23 GB); LLaDA 2.0 Uni 16B runs at Q5 (22 GB).
Bottom line: which should you pick?
- Pick Qwen 3 VL 30B-A3B for long-context work (up to 256k tokens).
- Pick LLaDA 2.0 Uni 16B for lower VRAM and faster inference; pick Qwen 3 VL 30B-A3B for maximum headline quality.
- Pick Qwen 3 VL 30B-A3B if your workload is multilingual.
Which GPU should you buy to run Qwen 3 VL 30B-A3B?
To run Qwen 3 VL 30B-A3B locally at Q4, you need ~19 GB of VRAM. The best value for this is a RTX 4090 (24 GB VRAM).
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Frequently asked questions
What is the difference between Qwen 3 VL 30B-A3B and LLaDA 2.0 Uni 16B?
The headline differences: Qwen 3 VL 30B-A3B is a 30B model and LLaDA 2.0 Uni 16B is 16B; their context windows differ (256k vs 8k 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 VL 30B-A3B and LLaDA 2.0 Uni 16B run on a 24 GB GPU?
At a Q4 quantization, Qwen 3 VL 30B-A3B needs about 19 GB of VRAM and fits comfortably on a 24 GB GPU; LLaDA 2.0 Uni 16B needs about 18 GB and fits comfortably on a 24 GB GPU. LLaDA 2.0 Uni 16B is the lighter option for tight VRAM budgets.
Which is faster, Qwen 3 VL 30B-A3B or LLaDA 2.0 Uni 16B?
LLaDA 2.0 Uni 16B is the smaller model (16B vs 30B), so on the same hardware it runs faster and uses less memory. The larger model trades speed for headline quality.
What licenses do Qwen 3 VL 30B-A3B and LLaDA 2.0 Uni 16B use?
Qwen 3 VL 30B-A3B is licensed under Apache 2.0 and LLaDA 2.0 Uni 16B under Apache 2.0.
Which has the longer context window, Qwen 3 VL 30B-A3B or LLaDA 2.0 Uni 16B?
Qwen 3 VL 30B-A3B has the larger context window (256k vs 8k tokens), so it handles longer documents and codebases in a single prompt.