Qwen 3 30B-A3B vs Granite 4.0 H-Small 32B-A9B

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 Granite 4.0 H-Small 32B-A9B

IBM's hybrid Mamba-2 + MoE model with 32B total and 9B active parameters, engineered to slash long-context memory use by roughly 70% versus comparable transformers under Apache 2.0. Strengths: Hybrid Mamba-2 architecture cuts long-context memory by ~70%, MoE design keeps active params at 9B for fast inference, Apache 2.0 with no usage restrictions, Built with enterprise governance and provenance in mind.

How they compare

Qwen 3 30B-A3B comes from Alibaba and Granite 4.0 H-Small 32B-A9B from IBM, they belong to the Qwen and Granite 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 32B parameters, Granite 4.0 H-Small 32B-A9B is the larger of the two. 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 Granite 4.0 H-Small 32B-A9B leans toward chat, general, moe. Match the model to your dominant workload rather than to raw size.

On a typical mid-range GPU, Qwen 3 30B-A3B pushes roughly 40 tokens/sec versus 30, so it is the more responsive choice for interactive or high-volume use. For long-context work, Qwen 3 30B-A3B offers the bigger window (128k vs 125k 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 Granite 4.0 H-Small 32B-A9B requires Q4 ≈ 19 GB, Q5 ≈ 23 GB, Q8 ≈ 35 GB, FP16 ≈ 64 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 Granite 4.0 H-Small 32B-A9B 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 30 from Granite 4.0 H-Small 32B-A9B, scaling up to 100 and 75 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 Granite 4.0 H-Small 32B-A9B to the card you actually own:

• On a 24 GB GPU: Qwen 3 30B-A3B runs at Q5 (23 GB); Granite 4.0 H-Small 32B-A9B 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 30B-A3B for long-context work (up to 128k tokens).
• Pick Qwen 3 30B-A3B for lower VRAM and faster inference; pick Granite 4.0 H-Small 32B-A9B for maximum headline quality.
• Pick Qwen 3 30B-A3B if your workload is multilingual, reasoning.

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 Granite 4.0 H-Small 32B-A9B?

The headline differences: Qwen 3 30B-A3B is a 30B model and Granite 4.0 H-Small 32B-A9B is 32B; their context windows differ (128k vs 125k 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 Granite 4.0 H-Small 32B-A9B 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; Granite 4.0 H-Small 32B-A9B needs about 19 GB and fits comfortably on a 24 GB GPU. Both have the same Q4 footprint.

Which is faster, Qwen 3 30B-A3B or Granite 4.0 H-Small 32B-A9B?

Qwen 3 30B-A3B is the smaller model (30B vs 32B), 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 Granite 4.0 H-Small 32B-A9B use?

Qwen 3 30B-A3B is licensed under Apache 2.0 and Granite 4.0 H-Small 32B-A9B under Apache 2.0.

Which has the longer context window, Qwen 3 30B-A3B or Granite 4.0 H-Small 32B-A9B?

Qwen 3 30B-A3B has the larger context window (128k vs 125k tokens), so it handles longer documents and codebases in a single prompt.