Qwen 3 30B-A3B vs ERNIE 4.5 21B-A3B Thinking
Side-by-side specs, benchmarks, and a verdict by use case.
Updated 2026-07-13
| Spec | Qwen 3 30B-A3B | ERNIE 4.5 21B-A3B Thinking |
|---|---|---|
| Parameters | 30B | 21B |
| Author | Alibaba | Baidu |
| License | Apache 2.0 | Apache 2.0 |
| Context window | 0k | 0k |
| VRAM at Q4 | 19 GB | 13 GB |
| VRAM at Q5 | 23 GB | 16 GB |
| VRAM at Q8 | 35 GB | 23 GB |
| VRAM at FP16 | 62 GB | 42 GB |
| Use cases | chat, general, reasoning, multilingual, moe | reasoning, moe |
Verdict
Both models sit in a similar size class. The pick depends on tags, license, and benchmarks rather than raw parameter count.
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 ERNIE 4.5 21B-A3B Thinking
Baidu's compact reasoning MoE with 3B active parameters out of 21B total. Fast inference thanks to the small active set, with Chinese-language strength. Strengths: Around 13 GB VRAM at Q4, Compact MoE optimized for reasoning, Strong Chinese-language performance, 128k context window.
How they compare
Qwen 3 30B-A3B comes from Alibaba and ERNIE 4.5 21B-A3B Thinking from Baidu, they belong to the Qwen and ERNIE 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 21B parameters, Qwen 3 30B-A3B is the larger of the two. At Q4, ERNIE 4.5 21B-A3B Thinking fits in about 13 GB of VRAM versus 19 GB for the other — a 6 GB difference that matters on consumer GPUs.
The two models target different sweet spots: Qwen 3 30B-A3B is tuned for chat, general, reasoning, multilingual, moe, while ERNIE 4.5 21B-A3B Thinking leans toward reasoning, moe. Match the model to your dominant workload rather than to raw size.
The smaller model, ERNIE 4.5 21B-A3B Thinking, will generally generate tokens faster on the same hardware.
Memory, quantization & throughput
Across quantization levels, Qwen 3 30B-A3B requires Q4 ≈ 19 GB, Q5 ≈ 23 GB, Q8 ≈ 35 GB, FP16 ≈ 62 GB, while ERNIE 4.5 21B-A3B Thinking requires Q4 ≈ 13 GB, Q5 ≈ 16 GB, Q8 ≈ 23 GB, FP16 ≈ 42 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 ERNIE 4.5 21B-A3B Thinking about 18 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 ERNIE 4.5 21B-A3B Thinking, 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 ERNIE 4.5 21B-A3B Thinking to the card you actually own:
- On a 16 GB GPU: Qwen 3 30B-A3B does not fit; ERNIE 4.5 21B-A3B Thinking runs at Q5 (16 GB).
- On a 24 GB GPU: Qwen 3 30B-A3B runs at Q5 (23 GB); ERNIE 4.5 21B-A3B Thinking runs at Q8 (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 ERNIE 4.5 21B-A3B Thinking for lower VRAM and faster inference; pick Qwen 3 30B-A3B for maximum headline quality.
- Pick Qwen 3 30B-A3B if your workload is chat, general, multilingual.
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).
As an Amazon Associate, BestLLMfor earns from qualifying purchases, at no extra cost to you. It does not influence our independent rankings.
Frequently asked questions
What is the difference between Qwen 3 30B-A3B and ERNIE 4.5 21B-A3B Thinking?
The headline differences: Qwen 3 30B-A3B is a 30B model and ERNIE 4.5 21B-A3B Thinking is 21B. 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 ERNIE 4.5 21B-A3B Thinking 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; ERNIE 4.5 21B-A3B Thinking needs about 13 GB and fits comfortably on a 24 GB GPU. ERNIE 4.5 21B-A3B Thinking is the lighter option for tight VRAM budgets.
Which is faster, Qwen 3 30B-A3B or ERNIE 4.5 21B-A3B Thinking?
ERNIE 4.5 21B-A3B Thinking is the smaller model (21B 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 30B-A3B and ERNIE 4.5 21B-A3B Thinking use?
Qwen 3 30B-A3B is licensed under Apache 2.0 and ERNIE 4.5 21B-A3B Thinking under Apache 2.0.