Granite 4.0 H-Small 32B-A9BvsLLaDA 2.0 Uni 16B
Complete comparison between Granite 4.0 H-Small 32B-A9B (32B parameters, IBM) and LLaDA 2.0 Uni 16B (16B, Ant Group / inclusionAI). VRAM required by quantization, tokens per second measured on 4 reference GPUs, verdict by use case, license, and installation commands. All figures are calculated from catalog data—no copy-pasting between pages.
In brief
| Feature | Granite 4.0 H-Small 32B-A9B | LLaDA 2.0 Uni 16B |
|---|---|---|
| Settings | 32B | 16B |
| Family | Granite | LLaDA |
| Author | IBM | Ant Group / inclusionAI |
| Origin | US | CN |
| License | Apache 2.0 | Apache 2.0 |
| Context | 128,000 tokens | 8,192 tokens |
| Output | October 2025 | April 2026 |
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 | LLaDA 2.0 Uni 16B |
|---|---|---|
| Q4_K_M (lightweight) | 19 GB | 18 GB |
| Q5_K_M (balanced) | 23 GB | 22 GB |
| Q8 (near-lossless) | 35 GB | 30 GB |
| FP16 (max quality) | 64 GB | 47 GB |
| CPU-only RAM | 32 GB | 36 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 | LLaDA 2.0 Uni 16B |
|---|---|---|
| RTX 4090 (24 GB) | 75 tok/s · Q5_K_M | 130 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 | 60 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
LLaDA 2.0 Uni 16B
First open Apache 2.0 dLLM: MoE 16B/1B + 6.2B diffusion decoder. Unified text+vision. Released April 22, 2026.
- First open-source dLLM under Apache 2.0
- Unified text+vision+generation+editing
- Interleaved 'thinking' mode
- Apache 2.0
- Diffusion architecture not supported by Ollama/llama.cpp
- Requires Flash Attention 2 + CUDA 12.4
- 47 GB of VRAM during full generation
- 8k context only
Installation
Frequently asked questions
Which model runs best on RTX 4090 (24 GB): Granite 4.0 H-Small 32B-A9B or LLaDA 2.0 Uni 16B?
On an RTX 4090, Granite 4.0 H-Small 32B-A9B runs at Q5_K_M (~75 tok/s), LLaDA 2.0 Uni 16B in Q5_K_M (~130 tok/s). In pure throughput terms, LLaDA 2.0 Uni 16B wins. See the configurator to test your exact GPU.
Which uses the least VRAM?
In Q4_K_M, LLaDA 2.0 Uni 16B fits in 18 GB versus 19 GB for its rival — a 1 GB gap, significant if you're targeting an RTX 3060 or a 4060 Ti 8 GB.
Can these models be used in commercial production?
Granite 4.0 H-Small 32B-A9B is under Apache 2.0 — free for commercial use. LLaDA 2.0 Uni 16B 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: LLaDA 2.0 Uni 16B (16B). 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.