Description

RKLLM software stack can help users to quickly deploy AI models to Rockchip chips. The overall framework is as follows:

In order to use RKNPU, users need to first run the RKLLM-Toolkit tool on the computer, convert the trained model into an RKLLM format model, and then inference on the development board using the RKLLM C API.

• RKLLM-Toolkit is a software development kit for users to perform model conversionand quantization on PC.

• RKLLM Runtime provides C/C++ programming interfaces for Rockchip NPU platform to help users deploy RKLLM models and accelerate the implementation of LLM applications.

• RKNPU kernel driver is responsible for interacting with NPU hardware. It has been open source and can be found in the Rockchip kernel code.

Support Platform

• RK3588 Series
• RK3576 Series

Support Models

• LLAMA models
• TinyLLAMA models
• Qwen models
• Phi models
• ChatGLM3-6B
• Gemma models
• InternLM2 models
• MiniCPM models

Model Performance Benchmark

model	dtype	seqlen	max_context	new_tokens	TTFT(ms)	Tokens/s	memory(G)	platform
TinyLLAMA-1.1B	w4a16	64	320	256	345.00	21.10	0.77	RK3576
	w4a16_g128	64	320	256	410.00	18.50	0.8	RK3576
	w8a8	64	320	256	140.46	24.21	1.25	RK3588
	w8a8_g512	64	320	256	195.00	20.08	1.29	RK3588
Qwen2-1.5B	w4a16	64	320	256	512.00	14.40	1.75	RK3576
	w4a16_g128	64	320	256	550.00	12.75	1.76	RK3576
	w8a8	64	320	256	206.00	16.46	2.47	RK3588
	w8a8_g128	64	320	256	725.00	7.00	2.65	RK3588
Phi-3-3.8B	w4a16	64	320	256	975.00	6.60	2.16	RK3576
	w4a16_g128	64	320	256	1180.00	5.85	2.23	RK3576
	w8a8	64	320	256	516.00	7.44	3.88	RK3588
	w8a8_g512	64	320	256	610.00	6.13	3.95	RK3588
ChatGLM3-6B	w4a16	64	320	256	1168.00	4.62	3.86	RK3576
	w4a16_g128	64	320	256	1582.56	3.82	3.96	RK3576
	w8a8	64	320	256	800.00	4.95	6.69	RK3588
	w8a8_g128	64	320	256	2190.00	2.70	7.18	RK3588
Gemma2-2B	w4a16	64	320	256	628.00	8.00	3.63	RK3576
	w4a16_g128	64	320	256	776.20	7.40	3.63	RK3576
	w8a8	64	320	256	342.29	9.67	4.84	RK3588
	w8a8_g128	64	320	256	1055.00	5.49	5.14	RK3588
InternLM2-1.8B	w4a16	64	320	256	475.00	13.30	1.59	RK3576
	w4a16_g128	64	320	256	572.00	11.95	1.62	RK3576
	w8a8	64	320	256	205.97	15.66	2.38	RK3588
	w8a8_g512	64	320	256	298.00	12.66	2.45	RK3588
MiniCPM3-4B	w4a16	64	320	256	1397.00	4.80	2.7	RK3576
	w4a16_g128	64	320	256	1645.00	4.39	2.8	RK3576
	w8a8	64	320	256	702.18	6.15	4.65	RK3588
	w8a8_g128	64	320	256	1691.00	3.42	5.06	RK3588
llama3-8B	w4a16	64	320	256	1607.98	3.60	5.63	RK3576
	w4a16_g128	64	320	256	2010.00	3.00	5.76	RK3576
	w8a8	64	320	256	1128.00	3.79	9.21	RK3588
	w8a8_g512	64	320	256	1281.35	3.05	9.45	RK3588

• This performance data were collected based on the maximum CPU and NPU frequencies of each platform with version 1.1.0.
• The script for setting the frequencies is located in the scripts directory.

Download

You can download the latest package, docker image, example, documentation, and platform-tool from RKLLM_SDK, fetch code: rkllm

Note

• The modifications in version 1.1 are significant, making it incompatible with older version models. Please use the latest toolchain for model conversion and inference.

• The supported Python versions are:

  • Python 3.8

  • Python 3.10

• Latest version: v1.1.1

RKNN Toolkit2

If you want to deploy additional AI model, we have introduced a SDK called RKNN-Toolkit2. For details, please refer to:

https://github.com/airockchip/rknn-toolkit2

CHANGELOG

v1.1.0

• Support group-wise quantization (w4a16 group sizes of 32/64/128, w8a8 group sizes of 128/256/512).
• Support joint inference with LoRA model loading
• Support storage and preloading of prompt cache.
• Support gguf model conversion (currently only support q4_0 and fp16).
• Optimize initialization, prefill, and decode time.
• Support four input types: prompt, embedding, token, and multimodal.
• Add PC-based simulation accuracy testing and inference interface support for rkllm-toolkit.
• Add gdq algorithm to improve 4-bit quantization accuracy.
• Add mixed quantization algorithm, supporting a combination of grouped and non-grouped quantization based on specified ratios.
• Add support for models such as Llama3, Gemma2, and MiniCPM3.
• Resolve catastrophic forgetting issue when the number of tokens exceeds max_context.

for older version, please refer CHANGELOG