Granite 4.0 H-Small 32B-A9B vs Qwen 3 VL 30B-A3B
Side-by-side specs, benchmarks, and a verdict by use case.
Updated 2026-07-13
| Spec | Granite 4.0 H-Small 32B-A9B | Qwen 3 VL 30B-A3B |
|---|---|---|
| Parameters | 32B | 30B |
| Author | IBM | Alibaba |
| License | Apache 2.0 | Apache 2.0 |
| Context window | 0k | 0k |
| VRAM at Q4 | 19 GB | 19 GB |
| VRAM at Q5 | 23 GB | 23 GB |
| VRAM at Q8 | 35 GB | 35 GB |
| VRAM at FP16 | 64 GB | 62 GB |
| Use cases | chat, general, moe | vision, chat, general, moe, multilingual |
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 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.
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
Granite 4.0 H-Small 32B-A9B comes from IBM and Qwen 3 VL 30B-A3B from Alibaba, they belong to the Granite and Qwen 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 32B vs 30B 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: Granite 4.0 H-Small 32B-A9B is tuned for chat, general, 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.
On a typical mid-range GPU, Qwen 3 VL 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 VL 30B-A3B offers the bigger window (256k vs 125k tokens).
Memory, quantization & throughput
Across quantization levels, Granite 4.0 H-Small 32B-A9B requires Q4 ≈ 19 GB, Q5 ≈ 23 GB, Q8 ≈ 35 GB, FP16 ≈ 64 GB, while Qwen 3 VL 30B-A3B requires Q4 ≈ 19 GB, Q5 ≈ 23 GB, Q8 ≈ 35 GB, FP16 ≈ 62 GB. In practice Granite 4.0 H-Small 32B-A9B 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, Granite 4.0 H-Small 32B-A9B 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 30 tokens/sec from Granite 4.0 H-Small 32B-A9B and 40 from Qwen 3 VL 30B-A3B, scaling up to 75 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 Granite 4.0 H-Small 32B-A9B or Qwen 3 VL 30B-A3B to the card you actually own:
- On a 24 GB GPU: Granite 4.0 H-Small 32B-A9B runs at Q5 (23 GB); Qwen 3 VL 30B-A3B runs at Q5 (23 GB).
Bottom line: which should you pick?
- Pick Qwen 3 VL 30B-A3B for long-context work (up to 256k tokens).
- Pick Qwen 3 VL 30B-A3B for lower VRAM and faster inference; pick Granite 4.0 H-Small 32B-A9B for maximum headline quality.
- Pick Qwen 3 VL 30B-A3B if your workload is multilingual, vision.
Which GPU should you buy to run Granite 4.0 H-Small 32B-A9B?
To run Granite 4.0 H-Small 32B-A9B 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 Granite 4.0 H-Small 32B-A9B and Qwen 3 VL 30B-A3B?
The headline differences: Granite 4.0 H-Small 32B-A9B is a 32B model and Qwen 3 VL 30B-A3B is 30B; their context windows differ (125k 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 Granite 4.0 H-Small 32B-A9B and Qwen 3 VL 30B-A3B run on a 24 GB GPU?
At a Q4 quantization, Granite 4.0 H-Small 32B-A9B 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.
Which is faster, Granite 4.0 H-Small 32B-A9B or Qwen 3 VL 30B-A3B?
Qwen 3 VL 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 Granite 4.0 H-Small 32B-A9B and Qwen 3 VL 30B-A3B use?
Granite 4.0 H-Small 32B-A9B is licensed under Apache 2.0 and Qwen 3 VL 30B-A3B under Apache 2.0.
Which has the longer context window, Granite 4.0 H-Small 32B-A9B or Qwen 3 VL 30B-A3B?
Qwen 3 VL 30B-A3B has the larger context window (256k vs 125k tokens), so it handles longer documents and codebases in a single prompt.