Model reference · open weights

granite-3.2

LLMs ibm-granite Text gen 2 builds Open weights 11k dl/mo

granite-3.2 is an open-weight language model from IBM. granite-3.2-8b-instruct (BF16) weighs 16.3 GB; the smallest configuration that runs it is 2× RTX 3060 12 GB.

  • Granite-3.2-8B-Instruct is an 8.2-billion-parameter text-generation model developed by IBM for general instruction-following and reasoning tasks.
  • It supports a context length of 131,072 tokens and operates in 12 languages, including English, German, and Chinese.
  • The model is released under the Apache 2.0 license.

Summary of the ibm-granite/granite-3.2-8b-instruct model card, 2026-10-01

What it is

Released byIBM
Released2025-02-17
Parameters8.2B
VRAM16.3 GB for the weights

What it runs on

Memory and cards for granite-3.2-8b-instruct (BF16)

16.3 GBweights, file size
164 MBcache per 1K tokens
652 MBruntime overhead, at least
131,072 tokenscontext max
CardRequests at onceContext maxMemory
8K each32K each
RTX 3060 12 GB … RTX 4060 Ti 16 GB
2 smaller cards
———
RTX 3090 24 GB4138K23.4 GB
RTX 4090 24 GB4137K23.4 GB
RTX 5090 32 GB10283K31.0 GB
L40S 48 GB205all 128K44.0 GB
A100 80 GB4511all 128K78.2 GB
H100 80 GB4310all 128K78.1 GB
RTX PRO 6000 Blackwell 96 GB5413all 128K93.8 GB
DGX Spark (GB10) 128 GB unified6416all 128K107 GB
H200 141 GB8721all 128K138 GB
B200 180 GB11528all 128K176 GB
2× RTX 3060 12 GB
tensor parallel
4133K11.6 GB a card
2× RTX 4060 Ti 16 GB
tensor parallel
9278K15.4 GB a card
2× RTX 4090 24 GB
tensor parallel
215all 128K23.4 GB a card
2× RTX 3090 24 GB
tensor parallel
215all 128K23.4 GB a card
Memory needed at each load
Requests at once8K tokens each32K tokens each
118.3 GB22.4 GB
523.7 GB43.8 GB
827.7 GB59.9 GB
1638.5 GB103 GB
3259.9 GB189 GB
64103 GB361 GB

One card, with vLLM's small-card settings.

Builds

Sizes, precisions & builds

BuildParamsPrecisionWeightsSmallest setup
granite-3.2-8b-instruct (above) ↗ 8.2BBF16 16.3 GB2× RTX 3060 12 GB
granite-3.2-2b-instruct ↗ 2.5BBF16 5.1 GBRTX 3060 12 GB

From the model card

What IBM says about granite-3.2

Read the model card

Model Summary: Granite-3.2-8B-Instruct is an 8-billion-parameter, long-context AI model fine-tuned for thinking capabilities. Built on top of Granite-3.1-8B-Instruct, it has been trained using a mix of permissively licensed open-source datasets and internally generated synthetic data designed for reasoning tasks. The model allows controllability of its thinking capability, ensuring it is applied only when required.

Supported Languages: English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese. However, users may finetune this Granite model for languages beyond these 12 languages.

Intended Use: This model is designed to handle general instruction-following tasks and can be integrated into AI assistants across various domains, including business applications.

Capabilities

  • Thinking
  • Summarization
  • Text classification
  • Text extraction
  • Question-answering
  • Retrieval Augmented Generation (RAG)
  • Code related tasks
  • Function-calling tasks
  • Multilingual dialog use cases
  • Long-context tasks including long document/meeting summarization, long document QA, etc.

Generation: This is a simple example of how to use Granite-3.2-8B-Instruct model.

Install the following libraries:

pip install torch torchvision torchaudio
pip install accelerate
pip install transformers

Then, copy the snippet from the section that is relevant for your use case.

from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed
import torch

model_path="ibm-granite/granite-3.2-8b-instruct"
device="cuda"
model = AutoModelForCausalLM.from_pretrained(
        model_path,
        device_map=device,
        torch_dtype=torch.bfloat16,
    )
tokenizer = AutoTokenizer.from_pretrained(
        model_path
)

conv = [{"role": "user", "content":"You have 10 liters of a 30% acid solution. How many liters of a 70% acid solution must be added to achieve a 50% acid mixture?"}]

input_ids = tokenizer.apply_chat_template(conv, return_tensors="pt", thinking=True, return_dict=True, add_generation_prompt=True).to(device)

set_seed(42)
output = model.generate(
    **input_ids,
    max_new_tokens=8192,
)

prediction = tokenizer.decode(output[0, input_ids["input_ids"].shape[1]:], skip_special_tokens=True)
print(prediction)

Example Outputs

  • thinking=True
Here is my thought process:

1. We know we start with 10 liters of a 30% acid solution, so there's 3 liters of pure acid in it (10 liters * 0.3).
2. We need to determine how much of the 70% acid solution to add such that the final mixture is 50% acid.
3. Let's say x liters of the 70% acid solution are added. This will contribute 0.7x liters of pure acid.
4. The total volume of the new mixture will be 10 + x liters, and the total amount of pure acid will be 3 + 0.7x liters.
5. We need to set up an equation where (3 + 0.7x) / (10 + x) = 0.5 (since we want a 50% acid mixture).
6. Solving this equation for x will give us the volume of 70% solution needed.

Here is my response:

To solve this, we need to set up an equation based on the concentration of acid before and after mixing.

1. **Initial Acid Content**:
   - We start with 10 liters of a 30% acid solution.
   - Pure acid in this = 10 * 0.3 = 3 liters.

2. **Variable for Addition**:
   - Let x be the liters of 70% acid solution to be added.
   - Pure acid in x liters of 70% solution = 0.7x liters.

3. **Final Mixture Concentration**:
   - Total volume of mixture = 10 + x liters.
   - Total pure acid in the mixture = 3 (from initial solution) + 0.7x (from added solution).

4. **Setting up the equation for 50% acid concentration**:
   - We require that the ratio of pure acid to total volume is 0.5 (50%).
   - Therefore, the equation is:

   \[
   \frac{3 + 0.7x}{10 + x} = 0.5
   \]

5. **Solving the equation**:

   Multiply both sides by (10 + x) to clear the fraction:

   \[
   3 + 0.7x = 0.5(10 + x)
   \]

   Expanding the right side:

   \[
   3 + 0.7x = 5 + 0.5x
   \]

   Subtract 0.5x from both sides:

   \[
   3 + 0.2x = 5
   \]

   Subtract 3 from both sides:

   \[
   0.2x = 2
   \]

   Divide by 0.2:

   \[
   x = 10
   \]

So, 10 liters of the 70% acid solution must be added to the initial 10 liters of 30% solution to get a 50% acid mixture.

Let's verify:

- Initial acid: 3 liters
- Acid from added solution: 0.7 * 10 = 7 liters
- Total acid: 3 + 7 = 10 liters
- Total volume: 10 + 10 = 20 liters
- Acid percentage: \(\frac{10}{20} = 0.5 = 50\%\)

This confirms our solution.

Final answer: 10 liters of 70% acid solution should be added.
  • thinking=False
Let's denote the amount of 70% acid solution to be added as x liters.

We start with 10 liters of a 30% acid solution, which means there are 0.3 * 10 = 3 liters of pure acid in it.

We want to end up with a 50% acid solution after adding x liters of 70% acid solution. The total volume of the mixture will be 10 + x liters, and we want half of this volume to be pure acid.

So, the amount of pure acid in the final mixture should be 0.5 * (10 + x) liters.

The amount of pure acid in the final mixture comes from two sources: the original 30% solution and the added 70% solution. So, we can set up the following equation:

3 (from the original solution) + 0.7x (from the added solution) = 0.5 * (10 + x)

Now, let's solve for x:

3 + 0.7x = 5 + 0.5x
0.7x - 0.5x = 5 - 3
0.2x = 2
x = 2 / 0.2
x = 10

So, you need to add 10 liters of a 70% acid solution to the 10 liters of a 30% acid solution to get a 50% acid mixture.

Evaluation Results:

Training Data: Overall, our training data is largely comprised of two key sources: (1) publicly available datasets with permissive license, (2)

Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.

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