Granite 4.0 H-Tiny 7B-A1B vs Mistral Nemo 12B Instruct

The two models at a glance

About Granite 4.0 H-Tiny 7B-A1B

IBM's edge-class hybrid MoE with 7B total and only 1B active parameters — Apache 2.0 licensed and built for embedded and low-cost serving. Strengths: Extremely low compute cost per token via 1B active params, Apache 2.0 license with no commercial strings attached, 128k context handled efficiently thanks to hybrid Mamba-2, Tiny memory footprint suits edge and serverless deploys.

About Mistral Nemo 12B Instruct

Mistral AI and NVIDIA's co-developed 12B instruct model with 128k context, the Tekken tokenizer, and strong European multilingual coverage. Strengths: 128k context window, Strong European multilingual performance, Apache 2.0 license, Efficient Tekken tokenizer reduces token counts.

How they compare

Granite 4.0 H-Tiny 7B-A1B comes from IBM and Mistral Nemo 12B Instruct from Mistral AI, they belong to the Granite and Mistral 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 7B vs 12B parameters, Mistral Nemo 12B Instruct is the larger of the two. At Q4, Granite 4.0 H-Tiny 7B-A1B fits in about 4 GB of VRAM versus 7 GB for the other — a 3 GB difference that matters on consumer GPUs.

The two models target different sweet spots: Granite 4.0 H-Tiny 7B-A1B is tuned for chat, general, moe, small, while Mistral Nemo 12B Instruct leans toward chat, general, multilingual, fr. Match the model to your dominant workload rather than to raw size.

On a typical mid-range GPU, Granite 4.0 H-Tiny 7B-A1B pushes roughly 180 tokens/sec versus 25, so it is the more responsive choice for interactive or high-volume use.

Memory, quantization & throughput

Across quantization levels, Granite 4.0 H-Tiny 7B-A1B requires Q4 ≈ 4 GB, Q5 ≈ 5 GB, Q8 ≈ 7 GB, FP16 ≈ 14 GB, while Mistral Nemo 12B Instruct requires Q4 ≈ 7 GB, Q5 ≈ 9 GB, Q8 ≈ 13 GB, FP16 ≈ 24 GB. In practice Granite 4.0 H-Tiny 7B-A1B fits an 8 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-Tiny 7B-A1B needs roughly 8 GB of system RAM to run on CPU and Mistral Nemo 12B Instruct about 16 GB — workable for offline use but far slower than GPU inference. On a mid-range GPU you can expect on the order of 180 tokens/sec from Granite 4.0 H-Tiny 7B-A1B and 25 from Mistral Nemo 12B Instruct, scaling up to 350 and 70 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-Tiny 7B-A1B or Mistral Nemo 12B Instruct to the card you actually own:

• On a 8 GB GPU: Granite 4.0 H-Tiny 7B-A1B runs at Q8 (7 GB); Mistral Nemo 12B Instruct runs at Q4 (7 GB).
• On a 12 GB GPU: Granite 4.0 H-Tiny 7B-A1B runs at Q8 (7 GB); Mistral Nemo 12B Instruct runs at Q5 (9 GB).
• On a 16 GB GPU: Granite 4.0 H-Tiny 7B-A1B runs at FP16 (14 GB); Mistral Nemo 12B Instruct runs at Q8 (13 GB).
• On a 24 GB GPU: Granite 4.0 H-Tiny 7B-A1B runs at FP16 (14 GB); Mistral Nemo 12B Instruct runs at FP16 (24 GB).

Benchmark scores

Reported benchmarks for Mistral Nemo 12B Instruct: MMLU 68, HellaSwag 83.5, Winogrande 76.8.

Bottom line: which should you pick?

• Pick Granite 4.0 H-Tiny 7B-A1B for lower VRAM and faster inference; pick Mistral Nemo 12B Instruct for maximum headline quality.
• Pick Granite 4.0 H-Tiny 7B-A1B if your workload is moe, small.
• Pick Mistral Nemo 12B Instruct if your workload is fr, multilingual.

Which GPU should you buy to run Mistral Nemo 12B Instruct?

To run Mistral Nemo 12B Instruct locally at Q4, you need ~7 GB of VRAM. The best value for this is a RTX 5060 (8 GB VRAM).

Frequently asked questions

What is the difference between Granite 4.0 H-Tiny 7B-A1B and Mistral Nemo 12B Instruct?

The headline differences: Granite 4.0 H-Tiny 7B-A1B is a 7B model and Mistral Nemo 12B Instruct is 12B. 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-Tiny 7B-A1B and Mistral Nemo 12B Instruct run on a 24 GB GPU?

At a Q4 quantization, Granite 4.0 H-Tiny 7B-A1B needs about 4 GB of VRAM and fits comfortably on a 24 GB GPU; Mistral Nemo 12B Instruct needs about 7 GB and fits comfortably on a 24 GB GPU. Granite 4.0 H-Tiny 7B-A1B is the lighter option for tight VRAM budgets.

Which is faster, Granite 4.0 H-Tiny 7B-A1B or Mistral Nemo 12B Instruct?

Granite 4.0 H-Tiny 7B-A1B is the smaller model (7B vs 12B), 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-Tiny 7B-A1B and Mistral Nemo 12B Instruct use?

Granite 4.0 H-Tiny 7B-A1B is licensed under Apache 2.0 and Mistral Nemo 12B Instruct under Apache 2.0.