Granite 4.0 H-Small 32B-A9BvsQwen 3 VL 30B-A3B
Complete comparison between Granite 4.0 H-Small 32B-A9B (32B parameters, IBM) and Qwen 3 VL 30B-A3B (30B, Alibaba). VRAM required by quantization, tokens per second measured on 4 reference GPUs, verdict by use case, license, installation commands. All figures are calculated from catalog data — no copy-pasting between pages.
In brief
| Feature | Granite 4.0 H-Small 32B-A9B | Qwen 3 VL 30B-A3B |
|---|---|---|
| Settings | 32B | 30B |
| Family | Granite | Qwen |
| Author | IBM | Alibaba |
| Origin | US | CN |
| License | Apache 2.0 | Apache 2.0 |
| Context | 128,000 tokens | 262,144 tokens |
| Output | October 2025 | May 2025 |
Memory footprint
Approximate VRAM required for inference with an average context window. The winner (in green) is the model that uses less — smaller models have the edge.
| Quantization | Granite 4.0 H-Small 32B-A9B | Qwen 3 VL 30B-A3B |
|---|---|---|
| Q4_K_M (lightweight) | 19 GB | 19 GB |
| Q5_K_M (balanced) | 23 GB | 23 GB |
| Q8 (near-lossless) | 35 GB | 35 GB |
| FP16 (max quality) | 64 GB | 62 GB |
| CPU-only RAM | 32 GB | 32 GB |
Estimated speed (tokens/second)
Estimates based on the best quantization that can fit on each GPU. Actual figures depend on the prompt, context, and engine (llama.cpp, vLLM, MLX). Methodology.
| Reference GPU | Granite 4.0 H-Small 32B-A9B | Qwen 3 VL 30B-A3B |
|---|---|---|
| RTX 4090 (24 GB) | 75 tok/s · Q5_K_M | 100 tok/s · Q5_K_M |
| RTX 4080 (16 GB) | ✗ too heavy | ✗ too heavy |
| RTX 3060 12GB (12 GB) | ✗ too heavy | ✗ too heavy |
| Apple M4 Pro (48 GB) (36 GB) | 30 tok/s · Q8 | 40 tok/s · Q8 |
Verdict by use case
For each common use case, we indicate which of the two is better suited based on its tags, size, and specialization.
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Strengths and weaknesses
Granite 4.0 H-Small 32B-A9B
Mamba-2 + MoE 32B/9B hybrid. ~70% less RAM in long contexts. Apache 2.0.
- 70% less RAM in long-context workloads
- Apache 2.0
- Enterprise-ready
- Recent llama.cpp support required
Installation
Qwen 3 VL 30B-A3B
Vision MoE with 30B/3B active. Vision sweet spot Qwen 3. 256k ctx.
- 262k multimodal context
- 19 GB VRAM Q4
- Efficient MoE
- Apache 2.0
- Worse vision than the 235B
- Fewer fine-tunes than Qwen2-VL
Installation
Frequently asked questions
Which model runs best on RTX 4090 (24 GB): Granite 4.0 H-Small 32B-A9B or Qwen 3 VL 30B-A3B?
On an RTX 4090, Granite 4.0 H-Small 32B-A9B runs at Q5_K_M (~75 tok/s), Qwen 3 VL 30B-A3B in Q5_K_M (~100 tok/s). In pure throughput, Qwen 3 VL 30B-A3B wins. See the configurator to test your exact GPU.
Which uses the least VRAM?
Both require the same VRAM in Q4 (19 GB). The choice then comes down to the license or perceived quality.
Can these models be used in commercial production?
Granite 4.0 H-Small 32B-A9B is under Apache 2.0 — free for commercial use. Qwen 3 VL 30B-A3B is under Apache 2.0 — also free to use. For SaaS, prefer Apache 2.0 or MIT.
Which one should you choose in 2026?
Depends on your primary constraint. Smaller / faster: Qwen 3 VL 30B-A3B (30B). More capable: Granite 4.0 H-Small 32B-A9B (32B). If you're unsure, run the configurator with your GPU and use case — it will decide based on both.
Alternatives to consider
If neither works for you, here are some neighboring models you could explore.