Granite 4.0 H-Small 32B-A9B vs LLaDA 2.0 Uni 16B

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 LLaDA 2.0 Uni 16B

Ant Group's first open Apache 2.0 diffusion LLM: a 16B/1B MoE paired with a 6.2B diffusion decoder, unifying text and vision generation and editing. Released April 2026. Strengths: The first Apache 2.0 open diffusion LLM, Unified text, vision, generation, and editing, Interleaved 'thinking' mode during diffusion, Decoder-turbo distillation runs 8 diffusion steps instead of 50.

How they compare

Granite 4.0 H-Small 32B-A9B comes from IBM and LLaDA 2.0 Uni 16B from Ant Group / inclusionAI, they belong to the Granite and LLaDA 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 16B parameters, Granite 4.0 H-Small 32B-A9B is the larger of the two. At Q4, LLaDA 2.0 Uni 16B fits in about 18 GB of VRAM versus 19 GB for the other — a 1 GB difference that matters on consumer GPUs.

The two models target different sweet spots: Granite 4.0 H-Small 32B-A9B is tuned for chat, general, moe, while LLaDA 2.0 Uni 16B leans toward chat, vision, general, moe. Match the model to your dominant workload rather than to raw size.

On a typical mid-range GPU, LLaDA 2.0 Uni 16B pushes roughly 60 tokens/sec versus 30, so it is the more responsive choice for interactive or high-volume use. For long-context work, Granite 4.0 H-Small 32B-A9B offers the bigger window (125k vs 8k 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 LLaDA 2.0 Uni 16B requires Q4 ≈ 18 GB, Q5 ≈ 22 GB, Q8 ≈ 30 GB, FP16 ≈ 47 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 LLaDA 2.0 Uni 16B about 36 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 60 from LLaDA 2.0 Uni 16B, scaling up to 75 and 130 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 LLaDA 2.0 Uni 16B to the card you actually own:

• On a 24 GB GPU: Granite 4.0 H-Small 32B-A9B runs at Q5 (23 GB); LLaDA 2.0 Uni 16B runs at Q5 (22 GB).

Bottom line: which should you pick?

• Pick Granite 4.0 H-Small 32B-A9B for long-context work (up to 125k tokens).
• Pick LLaDA 2.0 Uni 16B for lower VRAM and faster inference; pick Granite 4.0 H-Small 32B-A9B for maximum headline quality.
• Pick LLaDA 2.0 Uni 16B if your workload is 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).

Frequently asked questions

What is the difference between Granite 4.0 H-Small 32B-A9B and LLaDA 2.0 Uni 16B?

The headline differences: Granite 4.0 H-Small 32B-A9B is a 32B model and LLaDA 2.0 Uni 16B is 16B; their context windows differ (125k vs 8k 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 LLaDA 2.0 Uni 16B 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; LLaDA 2.0 Uni 16B needs about 18 GB and fits comfortably on a 24 GB GPU. LLaDA 2.0 Uni 16B is the lighter option for tight VRAM budgets.

Which is faster, Granite 4.0 H-Small 32B-A9B or LLaDA 2.0 Uni 16B?

LLaDA 2.0 Uni 16B is the smaller model (16B 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 LLaDA 2.0 Uni 16B use?

Granite 4.0 H-Small 32B-A9B is licensed under Apache 2.0 and LLaDA 2.0 Uni 16B under Apache 2.0.

Which has the longer context window, Granite 4.0 H-Small 32B-A9B or LLaDA 2.0 Uni 16B?

Granite 4.0 H-Small 32B-A9B has the larger context window (125k vs 8k tokens), so it handles longer documents and codebases in a single prompt.