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GatekeeperZA/Llama-3.2-3B-Instruct-RKLLM-v1.2.3
Llama-3.2-3B-Instruct-RKLLM-v1.2.3 is a machine learning model from GatekeeperZA. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for rkllm. The card lists the license as llama3.2.
RKLLM conversion of meta-llama/Llama-3.2-3B-Instruct for Rockchip RK3588 NPU inference.
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From the Hugging Face model README
RKLLM conversion of meta-llama/Llama-3.2-3B-Instruct for Rockchip RK3588 NPU inference.
Converted with RKLLM Toolkit v1.2.3. This is a standard instruct model — it does not produce <think> reasoning blocks.
| Property | Value |
|---|---|
| Base Model | meta-llama/Llama-3.2-3B-Instruct |
| Toolkit Version | RKLLM Toolkit v1.2.3 |
| Runtime Version | RKLLM Runtime ≥ v1.2.1 (v1.2.3 recommended) |
| Quantization | w8a8 (8-bit weights, 8-bit activations) |
| Quantization Algorithm | normal |
| Target Platform | RK3588 |
| NPU Cores | 3 |
| Max Context Length | 8192 tokens |
| Optimization Level | 0 |
| Thinking Mode | ❌ Not supported |
| Languages | English (+ multilingual inherited from Llama 3.2) |
Llama 3.2 3B Instruct is Meta's latest compact instruction model. It brings a different architecture and training lineage to the RK3588 NPU lineup — strong at instruction following, coding, and general reasoning without the overhead of a thinking/reasoning mode.
At ~3B parameters it sits between the 1.7B and 4B Qwen3 models, offering a useful middle ground.
# Clone the runtime
git clone https://github.com/airockchip/rknn-llm.git
cd rknn-llm/examples/rkllm_api_demo
# Run (aarch64)
./build/rkllm_api_demo /path/to/Llama-3.2-3B-Instruct-rk3588-w8a8.rkllm 4096 8192
Download and place in ~/models/:
mkdir -p ~/models/Llama-3.2-3B-Instruct
cd ~/models/Llama-3.2-3B-Instruct
git lfs install && git clone https://huggingface.co/GatekeeperZA/Llama-3.2-3B-Instruct-RKLLM-v1.2.3 .
The server auto-detects the model as instruct capability (no thinking). Use with GatekeeperZA/RKLLM-API-Server.
from rkllm.api import RKLLM
llm = RKLLM()
llm.load_huggingface(model="meta-llama/Llama-3.2-3B-Instruct", device="cpu", dtype="float32")
llm.build(
do_quantization=True,
optimization_level=0,
quantized_dtype="w8a8",
quantized_algorithm="normal",
target_platform="rk3588",
num_npu_core=3,
max_context=8192,
)
llm.export_rkllm("./Llama-3.2-3B-Instruct-rk3588-w8a8.rkllm")
WSL2 note: Requires ≥16GB WSL2 memory (
memory=16GBin~/.wslconfig). Write the output to a native Linux path (/home/user/) first, then copy to/mnt/— writing directly to the Windows mount triggers OOM during the export phase.
| File | Description |
|---|---|
Llama-3.2-3B-Instruct-rk3588-w8a8.rkllm | Quantized model for RK3588 NPU |