airockchip/rknn-llm

Health v2 · maintenance only

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1644 stars · 219 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

RKLLM is Rockchip's software stack for converting, quantizing, and running large language models on Rockchip NPU chips such as RK3588 and RK3576. It includes a Python toolkit for PC-side model conversion and a C/C++ runtime for on-device inference.

Use cases

• run an LLM locally on an RK3588 board
• convert a HuggingFace model like Qwen or Llama to RKLLM format
• quantize large language models for Rockchip NPU inference
• build a multimodal vision-language demo on embedded hardware
• deploy chat models offline on edge devices with no cloud connection

When to choose

• you are deploying LLMs on Rockchip RK3588/RK3576/RK3562 hardware
• you need fully local, offline inference on embedded NPU devices
• you want to convert and quantize popular open models (Qwen, Llama, Gemma, Phi) for NPU acceleration

When to avoid

• you are targeting GPUs, CPUs, or non-Rockchip accelerators
• you need server-scale LLM serving with high throughput
• your hardware lacks a Rockchip NPU

Facets

library · maturity active

llm-inference machine-learning sdk serialization large-language-models machine-learning embedded-systems hardware embedded python cpp rockchip npu rk3588 model-conversion quantization edge-ai on-device-inference linux

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markdown · JSON · MCP: product_card(name="airockchip/rknn-llm")