Granite 4.0 H-Small 32B-A9B vs gpt-oss 20B

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 gpt-oss 20B

OpenAI's compact open-weight MoE with 3.6B active out of 21B total parameters. Matches o3-mini on a laptop-class GPU under Apache 2.0. Strengths: Apache 2.0 with full commercial freedom, Around 13 GB VRAM at Q4 — runs on a 16 GB card, OpenAI quality in an accessible footprint, Native 128k context.

How they compare

Granite 4.0 H-Small 32B-A9B comes from IBM and gpt-oss 20B from OpenAI, they belong to the Granite and gpt-oss 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 21B parameters, Granite 4.0 H-Small 32B-A9B is the larger of the two. At Q4, gpt-oss 20B fits in about 13 GB of VRAM versus 19 GB for the other — a 6 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 gpt-oss 20B leans toward chat, general, reasoning, moe, small. Match the model to your dominant workload rather than to raw size.

On a typical mid-range GPU, gpt-oss 20B pushes roughly 55 tokens/sec versus 30, so it is the more responsive choice for interactive or high-volume use.

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 gpt-oss 20B requires Q4 ≈ 13 GB, Q5 ≈ 16 GB, Q8 ≈ 23 GB, FP16 ≈ 42 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 gpt-oss 20B about 18 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 55 from gpt-oss 20B, 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 gpt-oss 20B to the card you actually own:

• On a 16 GB GPU: Granite 4.0 H-Small 32B-A9B does not fit; gpt-oss 20B runs at Q5 (16 GB).
• On a 24 GB GPU: Granite 4.0 H-Small 32B-A9B runs at Q5 (23 GB); gpt-oss 20B runs at Q8 (23 GB).

Bottom line: which should you pick?

• Pick gpt-oss 20B for lower VRAM and faster inference; pick Granite 4.0 H-Small 32B-A9B for maximum headline quality.
• Pick gpt-oss 20B if your workload is reasoning, small.

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 gpt-oss 20B?

The headline differences: Granite 4.0 H-Small 32B-A9B is a 32B model and gpt-oss 20B is 21B. 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 gpt-oss 20B 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; gpt-oss 20B needs about 13 GB and fits comfortably on a 24 GB GPU. gpt-oss 20B is the lighter option for tight VRAM budgets.

Which is faster, Granite 4.0 H-Small 32B-A9B or gpt-oss 20B?

gpt-oss 20B is the smaller model (21B 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 gpt-oss 20B use?

Granite 4.0 H-Small 32B-A9B is licensed under Apache 2.0 and gpt-oss 20B under Apache 2.0.