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 ---
tags:
- w4a16
- int4
- vllm
license: apache-2.0
license_link: https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md
language:
- en
base_model: ibm-granite/granite-3.1-8b-instruct
library_name: transformers
---
<h1 style="display: flex; align-items: center; gap: 10px; margin: 0;">
granite-3.1-8b-instruct-quantized.w4a16
<img src="https://www.redhat.com/rhdc/managed-files/Catalog-Validated_model_0.png" alt="Model Icon" width="40" style="margin: 0; padding: 0;" />
</h1>
<a href="https://www.redhat.com/en/products/ai/validated-models" target="_blank" style="margin: 0; padding: 0;">
<img src="https://www.redhat.com/rhdc/managed-files/Validated_badge-Dark.png" alt="Validated Badge" width="250" style="margin: 0; padding: 0;" />
</a>
## Model Overview
- **Model Architecture:** granite-3.1-8b-instruct
- **Input:** Text
- **Output:** Text
- **Model Optimizations:**
- **Weight quantization:** INT4
- **Activation quantization:** INT4
- **Release Date:** 1/8/2025
- **Version:** 1.0
- **Model Developers:** Neural Magic
Quantized version of [ibm-granite/granite-3.1-8b-instruct](https://huggingface.co/ibm-granite/granite-3.1-8b-instruct).
It achieves an average score of 69.81 on the [OpenLLM](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) benchmark (version 1), whereas the unquantized model achieves 70.30.
### Model Optimizations
This model was obtained by quantizing the weights of [ibm-granite/granite-3.1-8b-instruct](https://huggingface.co/ibm-granite/granite-3.1-8b-instruct) to INT4 data type, ready for inference with vLLM >= 0.5.2.
This optimization reduces the number of bits per parameter from 16 to 4, reducing the disk size and GPU memory requirements by approximately 75%. Only the weights of the linear operators within transformers blocks are quantized.
## Deployment
### Use with vLLM
This model can be deployed efficiently using the [vLLM](https://docs.vllm.ai/en/latest/) backend, as shown in the example below.
```python
from transformers import AutoTokenizer
from vllm import LLM, SamplingParams
max_model_len, tp_size = 4096, 1
model_name = "neuralmagic/granite-3.1-8b-instruct-quantized.w4a16"
tokenizer = AutoTokenizer.from_pretrained(model_name)
llm = LLM(model=model_name, tensor_parallel_size=tp_size, max_model_len=max_model_len, trust_remote_code=True)
sampling_params = SamplingParams(temperature=0.3, max_tokens=256, stop_token_ids=[tokenizer.eos_token_id])
messages_list = [
[{"role": "user", "content": "Who are you? Please respond in pirate speak!"}],
]
prompt_token_ids = [tokenizer.apply_chat_template(messages, add_generation_prompt=True) for messages in messages_list]
outputs = llm.generate(prompt_token_ids=prompt_token_ids, sampling_params=sampling_params)
generated_text = [output.outputs[0].text for output in outputs]
print(generated_text)
```
vLLM also supports OpenAI-compatible serving. See the [documentation](https://docs.vllm.ai/en/latest/) for more details.
<details>
<summary>Deploy on <strong>Red Hat AI Inference Server</strong></summary>
```bash
podman run --rm -it --device nvidia.com/gpu=all -p 8000:8000 \
--ipc=host \
--env "HUGGING_FACE_HUB_TOKEN=$HF_TOKEN" \
--env "HF_HUB_OFFLINE=0" -v ~/.cache/vllm:/home/vllm/.cache \
--name=vllm \
registry.access.redhat.com/rhaiis/rh-vllm-cuda \
vllm serve \
--tensor-parallel-size 8 \
--max-model-len 32768 \
--enforce-eager --model RedHatAI/granite-3.1-8b-instruct-quantized.w4a16
```
​​See [Red Hat AI Inference Server documentation](https://docs.redhat.com/en/documentation/red_hat_ai_inference_server/) for more details.
</details>
<details>
<summary>Deploy on <strong>Red Hat Enterprise Linux AI</strong></summary>
```bash
# Download model from Red Hat Registry via docker
# Note: This downloads the model to ~/.cache/instructlab/models unless --model-dir is specified.
ilab model download --repository docker://registry.redhat.io/rhelai1/granite-3-1-8b-instruct-quantized-w4a16:1.5
```
```bash
# Serve model via ilab
ilab model serve --model-path ~/.cache/instructlab/models/granite-3-1-8b-instruct-quantized-w4a16 -- --trust-remote-code
# Chat with model
ilab model chat --model ~/.cache/instructlab/models/granite-3-1-8b-instruct-quantized-w4a16
```
See [Red Hat Enterprise Linux AI documentation](https://docs.redhat.com/en/documentation/red_hat_enterprise_linux_ai/1.4) for more details.
</details>
<details>
<summary>Deploy on <strong>Red Hat Openshift AI</strong></summary>
```python
# Setting up vllm server with ServingRuntime
# Save as: vllm-servingruntime.yaml
apiVersion: serving.kserve.io/v1alpha1
kind: ServingRuntime
metadata:
name: vllm-cuda-runtime # OPTIONAL CHANGE: set a unique name
annotations:
openshift.io/display-name: vLLM NVIDIA GPU ServingRuntime for KServe
opendatahub.io/recommended-accelerators: '["nvidia.com/gpu"]'
labels:
opendatahub.io/dashboard: 'true'
spec:
annotations:
prometheus.io/port: '8080'
prometheus.io/path: '/metrics'
multiModel: false
supportedModelFormats:
- autoSelect: true
name: vLLM
containers:
- name: kserve-container
image: quay.io/modh/vllm:rhoai-2.20-cuda # CHANGE if needed. If AMD: quay.io/modh/vllm:rhoai-2.20-rocm
command:
- python
- -m
- vllm.entrypoints.openai.api_server
args:
- "--port=8080"
- "--model=/mnt/models"
- "--served-model-name={{.Name}}"
env:
- name: HF_HOME
value: /tmp/hf_home
ports:
- containerPort: 8080
protocol: TCP
```
```python
# Attach model to vllm server. This is an NVIDIA template
# Save as: inferenceservice.yaml
apiVersion: serving.kserve.io/v1beta1
kind: InferenceService
metadata:
annotations:
openshift.io/display-name: granite-3-1-8b-instruct-quantized-w4a16 # OPTIONAL CHANGE
serving.kserve.io/deploymentMode: RawDeployment
name: granite-3-1-8b-instruct-quantized-w4a16 # specify model name. This value will be used to invoke the model in the payload
labels:
opendatahub.io/dashboard: 'true'
spec:
predictor:
maxReplicas: 1
minReplicas: 1
model:
args:
- '--trust-remote-code'
modelFormat:
name: vLLM
name: ''
resources:
limits:
cpu: '2' # this is model specific
memory: 8Gi # this is model specific
nvidia.com/gpu: '1' # this is accelerator specific
requests: # same comment for this block
cpu: '1'
memory: 4Gi
nvidia.com/gpu: '1'
runtime: vllm-cuda-runtime # must match the ServingRuntime name above
storageUri: oci://registry.redhat.io/rhelai1/modelcar-granite-3-1-8b-instruct-quantized-w4a16:1.5
tolerations:
- effect: NoSchedule
key: nvidia.com/gpu
operator: Exists
```
```bash
# make sure first to be in the project where you want to deploy the model
# oc project <project-name>
# apply both resources to run model
# Apply the ServingRuntime
oc apply -f vllm-servingruntime.yaml
# Apply the InferenceService
oc apply -f qwen-inferenceservice.yaml
```
```python
# Replace <inference-service-name> and <cluster-ingress-domain> below:
# - Run `oc get inferenceservice` to find your URL if unsure.
# Call the server using curl:
curl https://<inference-service-name>-predictor-default.<domain>/v1/chat/completions
-H "Content-Type: application/json" \
-d '{
"model": "granite-3-1-8b-instruct-quantized-w4a16",
"stream": true,
"stream_options": {
"include_usage": true
},
"max_tokens": 1,
"messages": [
{
"role": "user",
"content": "How can a bee fly when its wings are so small?"
}
]
}'
```
See [Red Hat Openshift AI documentation](https://docs.redhat.com/en/documentation/red_hat_openshift_ai/2025) for more details.
</details>
## Creation
This model was created with [llm-compressor](https://github.com/vllm-project/llm-compressor) by running the code snippet below.
<details>
<summary>Model Creation Code</summary>
```bash
python quantize.py --model_path ibm-granite/granite-3.1-8b-instruct --quant_path "output_dir/granite-3.1-8b-instruct-quantized.w4a16" --calib_size 1024 --dampening_frac 0.1 --observer mse --actorder static
```
```python
from datasets import load_dataset
from transformers import AutoTokenizer
from llmcompressor.modifiers.quantization import GPTQModifier
from llmcompressor.transformers import SparseAutoModelForCausalLM, oneshot, apply
import argparse
from compressed_tensors.quantization import QuantizationScheme, QuantizationArgs, QuantizationType, QuantizationStrategy
parser = argparse.ArgumentParser()
parser.add_argument('--model_path', type=str)
parser.add_argument('--quant_path', type=str)
parser.add_argument('--calib_size', type=int, default=256)
parser.add_argument('--dampening_frac', type=float, default=0.1)
parser.add_argument('--observer', type=str, default="minmax")
parser.add_argument('--actorder', type=str, default="dynamic")
args = parser.parse_args()
model = SparseAutoModelForCausalLM.from_pretrained(
args.model_path,
device_map="auto",
torch_dtype="auto",
use_cache=False,
trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(args.model_path)
NUM_CALIBRATION_SAMPLES = args.calib_size
DATASET_ID = "neuralmagic/LLM_compression_calibration"
DATASET_SPLIT = "train"
ds = load_dataset(DATASET_ID, split=DATASET_SPLIT)
ds = ds.shuffle(seed=42).select(range(NUM_CALIBRATION_SAMPLES))
def preprocess(example):
return {"text": example["text"]}
ds = ds.map(preprocess)
def tokenize(sample):
return tokenizer(
sample["text"],
padding=False,
truncation=False,
add_special_tokens=True,
)
ds = ds.map(tokenize, remove_columns=ds.column_names)
recipe = [
GPTQModifier(
targets=["Linear"],
ignore=["lm_head"],
scheme="w4a16",
dampening_frac=args.dampening_frac,
observer=args.observer,
actoder=args.actorder,
)
]
oneshot(
model=model,
dataset=ds,
recipe=recipe,
num_calibration_samples=args.calib_size,
max_seq_length=8196,
)
# Save to disk compressed.
model.save_pretrained(SAVE_DIR, save_compressed=True)
tokenizer.save_pretrained(SAVE_DIR)
```
</details>
## Evaluation
The model was evaluated on OpenLLM Leaderboard [V1](https://huggingface.co/spaces/open-llm-leaderboard-old/open_llm_leaderboard), OpenLLM Leaderboard [V2](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/) and on [HumanEval](https://github.com/neuralmagic/evalplus), using the following commands:
<details>
<summary>Evaluation Commands</summary>
OpenLLM Leaderboard V1:
```
lm_eval \
--model vllm \
--model_args pretrained="neuralmagic/granite-3.1-8b-instruct-quantized.w4a16",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=1,gpu_memory_utilization=0.8,enable_chunked_prefill=True,trust_remote_code=True \
--tasks openllm \
--write_out \
--batch_size auto \
--output_path output_dir \
--show_config
```
OpenLLM Leaderboard V2:
```
lm_eval \
--model vllm \
--model_args pretrained="neuralmagic/granite-3.1-8b-instruct-quantized.w4a16",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=1,gpu_memory_utilization=0.8,enable_chunked_prefill=True,trust_remote_code=True \
--tasks leaderboard \
--write_out \
--batch_size auto \
--output_path output_dir \
--show_config
```
#### HumanEval
##### Generation
```
python3 codegen/generate.py \
--model neuralmagic/granite-3.1-8b-instruct-quantized.w4a16 \
--bs 16 \
--temperature 0.2 \
--n_samples 50 \
--root "." \
--dataset humaneval
```
##### Sanitization
```
python3 evalplus/sanitize.py \
humaneval/neuralmagic--granite-3.1-8b-instruct-quantized.w4a16_vllm_temp_0.2
```
##### Evaluation
```
evalplus.evaluate \
--dataset humaneval \
--samples humaneval/neuralmagic--granite-3.1-8b-instruct-quantized.w4a16_vllm_temp_0.2-sanitized
```
</details>
### Accuracy
<table>
<thead>
<tr>
<th>Category</th>
<th>Metric</th>
<th>ibm-granite/granite-3.1-8b-instruct</th>
<th>neuralmagic/granite-3.1-8b-instruct-quantized.w4a16</th>
<th>Recovery (%)</th>
</tr>
</thead>
<tbody>
<tr>
<td rowspan="7"><b>OpenLLM V1</b></td>
<td>ARC-Challenge (Acc-Norm, 25-shot)</td>
<td>66.81</td>
<td>66.81</td>
<td>100.00</td>
</tr>
<tr>
<td>GSM8K (Strict-Match, 5-shot)</td>
<td>64.52</td>
<td>65.66</td>
<td>101.77</td>
</tr>
<tr>
<td>HellaSwag (Acc-Norm, 10-shot)</td>
<td>84.18</td>
<td>83.62</td>
<td>99.33</td>
</tr>
<tr>
<td>MMLU (Acc, 5-shot)</td>
<td>65.52</td>
<td>64.25</td>
<td>98.06</td>
</tr>
<tr>
<td>TruthfulQA (MC2, 0-shot)</td>
<td>60.57</td>
<td>60.17</td>
<td>99.34</td>
</tr>
<tr>
<td>Winogrande (Acc, 5-shot)</td>
<td>80.19</td>
<td>78.37</td>
<td>97.73</td>
</tr>
<tr>
<td><b>Average Score</b></td>
<td><b>70.30</b></td>
<td><b>69.81</b></td>
<td><b>99.31</b></td>
</tr>
<tr>
<td rowspan="7"><b>OpenLLM V2</b></td>
<td>IFEval (Inst Level Strict Acc, 0-shot)</td>
<td>74.01</td>
<td>73.14</td>
<td>98.82</td>
</tr>
<tr>
<td>BBH (Acc-Norm, 3-shot)</td>
<td>53.19</td>
<td>51.52</td>
<td>96.86</td>
</tr>
<tr>
<td>Math-Hard (Exact-Match, 4-shot)</td>
<td>14.77</td>
<td>16.66</td>
<td>112.81</td>
</tr>
<tr>
<td>GPQA (Acc-Norm, 0-shot)</td>
<td>31.76</td>
<td>29.91</td>
<td>94.17</td>
</tr>
<tr>
<td>MUSR (Acc-Norm, 0-shot)</td>
<td>46.01</td>
<td>45.75</td>
<td>99.44</td>
</tr>
<tr>
<td>MMLU-Pro (Acc, 5-shot)</td>
<td>35.81</td>
<td>34.23</td>
<td>95.59</td>
</tr>
<tr>
<td><b>Average Score</b></td>
<td><b>42.61</b></td>
<td><b>41.87</b></td>
<td><b>98.26</b></td>
</tr>
<tr>
<td rowspan="2"><b>Coding</b></td>
<td>HumanEval Pass@1</td>
<td>71.00</td>
<td>70.50</td>
<td><b>99.30</b></td>
</tr>
</tbody>
</table>
## Inference Performance
This model achieves up to 2.7x speedup in single-stream deployment and up to 1.5x speedup in multi-stream asynchronous deployment, depending on hardware and use-case scenario.
The following performance benchmarks were conducted with [vLLM](https://docs.vllm.ai/en/latest/) version 0.6.6.post1, and [GuideLLM](https://github.com/neuralmagic/guidellm).
<details>
<summary>Benchmarking Command</summary>
```
guidellm --model neuralmagic/granite-3.1-8b-instruct-quantized.w4a16 --target "http://localhost:8000/v1" --data-type emulated --data "prompt_tokens=<prompt_tokens>,generated_tokens=<generated_tokens>" --max seconds 360 --backend aiohttp_server
```
</details>
### Single-stream performance (measured with vLLM version 0.6.6.post1)
<table>
<tr>
<td></td>
<td></td>
<td></td>
<th style="text-align: center;" colspan="7" >Latency (s)</th>
</tr>
<tr>
<th>GPU class</th>
<th>Model</th>
<th>Speedup</th>
<th>Code Completion<br>prefill: 256 tokens<br>decode: 1024 tokens</th>
<th>Docstring Generation<br>prefill: 768 tokens<br>decode: 128 tokens</th>
<th>Code Fixing<br>prefill: 1024 tokens<br>decode: 1024 tokens</th>
<th>RAG<br>prefill: 1024 tokens<br>decode: 128 tokens</th>
<th>Instruction Following<br>prefill: 256 tokens<br>decode: 128 tokens</th>
<th>Multi-turn Chat<br>prefill: 512 tokens<br>decode: 256 tokens</th>
<th>Large Summarization<br>prefill: 4096 tokens<br>decode: 512 tokens</th>
</tr>
<tr>
<td style="vertical-align: middle;" rowspan="3" >A5000</td>
<td>granite-3.1-8b-instruct</td>
<td></td>
<td>28.3</td>
<td>3.7</td>
<td>28.8</td>
<td>3.8</td>
<td>3.6</td>
<td>7.2</td>
<td>15.7</td>
</tr>
<tr>
<td>granite-3.1-8b-instruct-quantized.w8a8</td>
<td>1.60</td>
<td>17.7</td>
<td>2.3</td>
<td>18.0</td>
<td>2.4</td>
<td>2.2</td>
<td>4.5</td>
<td>10.0</td>
</tr>
<tr>
<td>granite-3.1-8b-instruct-quantized.w4a16<br>(this model)</td>
<td>2.61</td>
<td>10.3</td>
<td>1.5</td>
<td>10.7</td>
<td>1.5</td>
<td>1.3</td>
<td>2.7</td>
<td>6.6</td>
</tr>
<tr>
<td style="vertical-align: middle;" rowspan="3" >A6000</td>
<td>granite-3.1-8b-instruct</td>
<td></td>
<td>25.8</td>
<td>3.4</td>
<td>26.2</td>
<td>3.4</td>
<td>3.3</td>
<td>6.5</td>
<td>14.2</td>
</tr>
<tr>
<td>granite-3.1-8b-instruct-quantized.w8a8</td>
<td>1.50</td>
<td>17.4</td>
<td>2.3</td>
<td>16.9</td>
<td>2.2</td>
<td>2.2</td>
<td>4.4</td>
<td>9.8</td>
</tr>
<tr>
<td>granite-3.1-8b-instruct-quantized.w4a16<br>(this model)</td>
<td>2.48</td>
<td>10.0</td>
<td>1.4</td>
<td>10.4</td>
<td>1.5</td>
<td>1.3</td>
<td>2.5</td>
<td>6.2</td>
</tr>
<tr>
<td style="vertical-align: middle;" rowspan="3" >A100</td>
<td>granite-3.1-8b-instruct</td>
<td></td>
<td>13.6</td>
<td>1.8</td>
<td>13.7</td>
<td>1.8</td>
<td>1.7</td>
<td>3.4</td>
<td>7.3</td>
</tr>
<tr>
<td>granite-3.1-8b-instruct-quantized.w8a8</td>
<td>1.31</td>
<td>10.4</td>
<td>1.3</td>
<td>10.5</td>
<td>1.4</td>
<td>1.3</td>
<td>2.6</td>
<td>5.6</td>
</tr>
<tr>
<td>granite-3.1-8b-instruct-quantized.w4a16<br>(this model)</td>
<td>1.80</td>
<td>7.3</td>
<td>1.0</td>
<td>7.4</td>
<td>1.0</td>
<td>0.9</td>
<td>1.9</td>
<td>4.3</td>
</tr>
<tr>
<td style="vertical-align: middle;" rowspan="3" >L40</td>
<td>granite-3.1-8b-instruct</td>
<td></td>
<td>25.1</td>
<td>3.2</td>
<td>25.3</td>
<td>3.2</td>
<td>3.2</td>
<td>6.3</td>
<td>13.4</td>
</tr>
<tr>
<td>granite-3.1-8b-instruct-FP8-dynamic</td>
<td>1.47</td>
<td>16.8</td>
<td>2.2</td>
<td>17.1</td>
<td>2.2</td>
<td>2.1</td>
<td>4.2</td>
<td>9.3</td>
</tr>
<tr>
<td>granite-3.1-8b-instruct-quantized.w4a16<br>(this model)</td>
<td>2.72</td>
<td>8.9</td>
<td>1.2</td>
<td>9.2</td>
<td>1.2</td>
<td>1.1</td>
<td>2.3</td>
<td>5.3</td>
</tr>
</table>
### Multi-stream asynchronous performance (measured with vLLM version 0.6.6.post1)
<table>
<tr>
<td></td>
<td></td>
<td></td>
<th style="text-align: center;" colspan="7" >Maximum Throughput (Queries per Second)</th>
</tr>
<tr>
<th>GPU class</th>
<th>Model</th>
<th>Speedup</th>
<th>Code Completion<br>prefill: 256 tokens<br>decode: 1024 tokens</th>
<th>Docstring Generation<br>prefill: 768 tokens<br>decode: 128 tokens</th>
<th>Code Fixing<br>prefill: 1024 tokens<br>decode: 1024 tokens</th>
<th>RAG<br>prefill: 1024 tokens<br>decode: 128 tokens</th>
<th>Instruction Following<br>prefill: 256 tokens<br>decode: 128 tokens</th>
<th>Multi-turn Chat<br>prefill: 512 tokens<br>decode: 256 tokens</th>
<th>Large Summarization<br>prefill: 4096 tokens<br>decode: 512 tokens</th>
</tr>
<tr>
<td style="vertical-align: middle;" rowspan="3" >A5000</td>
<td>granite-3.1-8b-instruct</td>
<td></td>
<td>0.8</td>
<td>3.1</td>
<td>0.4</td>
<td>2.5</td>
<td>6.7</td>
<td>2.7</td>
<td>0.3</td>
</tr>
<tr>
<td>granite-3.1-8b-instruct-quantized.w8a8</td>
<td>1.71</td>
<td>1.3</td>
<td>5.2</td>
<td>0.9</td>
<td>4.0</td>
<td>10.5</td>
<td>4.4</td>
<td>0.5</td>
</tr>
<tr>
<td>granite-3.1-8b-instruct-quantized.w4a16<br>(this model)</td>
<td>1.46</td>
<td>1.3</td>
<td>3.9</td>
<td>0.8</td>
<td>2.9</td>
<td>8.2</td>
<td>3.6</td>
<td>0.5</td>
</tr>
<tr>
<td style="vertical-align: middle;" rowspan="3" >A6000</td>
<td>granite-3.1-8b-instruct</td>
<td></td>
<td>1.3</td>
<td>5.1</td>
<td>0.9</td>
<td>4.0</td>
<td>0.3</td>
<td>4.3</td>
<td>0.6</td>
</tr>
<tr>
<td>granite-3.1-8b-instruct-quantized.w8a8</td>
<td>1.39</td>
<td>1.8</td>
<td>7.0</td>
<td>1.3</td>
<td>5.6</td>
<td>14.0</td>
<td>6.3</td>
<td>0.8</td>
</tr>
<tr>
<td>granite-3.1-8b-instruct-quantized.w4a16<br>(this model)</td>
<td>1.09</td>
<td>1.9</td>
<td>4.8</td>
<td>1.0</td>
<td>3.8</td>
<td>10.0</td>
<td>5.0</td>
<td>0.6</td>
</tr>
<tr>
<td style="vertical-align: middle;" rowspan="3" >A100</td>
<td>granite-3.1-8b-instruct</td>
<td></td>
<td>3.1</td>
<td>10.7</td>
<td>2.1</td>
<td>8.5</td>
<td>20.6</td>
<td>9.6</td>
<td>1.4</td>
</tr>
<tr>
<td>granite-3.1-8b-instruct-quantized.w8a8</td>
<td>1.23</td>
<td>3.8</td>
<td>14.2</td>
<td>2.1</td>
<td>11.4</td>
<td>25.9</td>
<td>12.1</td>
<td>1.7</td>
</tr>
<tr>
<td>granite-3.1-8b-instruct-quantized.w4a16<br>(this model)</td>
<td>0.96</td>
<td>3.4</td>
<td>9.0</td>
<td>2.6</td>
<td>7.2</td>
<td>18.0</td>
<td>8.8</td>
<td>1.3</td>
</tr>
<tr>
<td style="vertical-align: middle;" rowspan="3" >L40</td>
<td>granite-3.1-8b-instruct</td>
<td></td>
<td>1.4</td>
<td>7.8</td>
<td>1.1</td>
<td>6.2</td>
<td>15.5</td>
<td>6.0</td>
<td>0.7</td>
</tr>
<tr>
<td>granite-3.1-8b-instruct-FP8-dynamic</td>
<td>1.12</td>
<td>2.1</td>
<td>7.4</td>
<td>1.3</td>
<td>5.9</td>
<td>15.3</td>
<td>6.9</td>
<td>0.8</td>
</tr>
<tr>
<td>granite-3.1-8b-instruct-quantized.w4a16<br>(this model)</td>
<td>1.29</td>
<td>2.4</td>
<td>8.9</td>
<td>1.4</td>
<td>7.1</td>
<td>17.8</td>
<td>7.8</td>
<td>1.0</td>
</tr>
</table>