PowerInfer/SmallThinker-21BA3B-Instruct

Introduction

  🤗 Hugging Face   |   🤖 ModelScope   |    📑 Technical Report       📚 Paper    |    💻 GitHub Repo   

SmallThinker is a family of on-device native Mixture-of-Experts (MoE) language models specially designed for local deployment, co-developed by the IPADS and School of AI at Shanghai Jiao Tong University and Zenergize AI. Designed from the ground up for resource-constrained environments, SmallThinker brings powerful, private, and low-latency AI directly to your personal devices, without relying on the cloud.

Paper

The model was presented in the paper SmallThinker: A Family of Efficient Large Language Models Natively Trained for Local Deployment.

Abstract

While frontier large language models (LLMs) continue to push capability boundaries, their deployment remains confined to GPU-powered cloud infrastructure. We challenge this paradigm with SmallThinker, a family of LLMs natively designed - not adapted - for the unique constraints of local devices: weak computational power, limited memory, and slow storage. Unlike traditional approaches that mainly compress existing models built for clouds, we architect SmallThinker from the ground up to thrive within these limitations. Our innovation lies in a deployment-aware architecture that transforms constraints into design principles. First, We introduce a two-level sparse structure combining fine-grained Mixture-of-Experts (MoE) with sparse feed-forward networks, drastically reducing computational demands without sacrificing model capacity. Second, to conquer the I/O bottleneck of slow storage, we design a pre-attention router that enables our co-designed inference engine to prefetch expert parameters from storage while computing attention, effectively hiding storage latency that would otherwise cripple on-device inference. Third, for memory efficiency, we utilize NoPE-RoPE hybrid sparse attention mechanism to slash KV cache requirements. We release SmallThinker-4B-A0.6B and SmallThinker-21B-A3B, which achieve state-of-the-art performance scores and even outperform larger LLMs. Remarkably, our co-designed system mostly eliminates the need for expensive GPU hardware: with Q4_0 quantization, both models exceed 20 tokens/s on ordinary consumer CPUs, while consuming only 1GB and 8GB of memory respectively. SmallThinker is publicly available at this http URL and this http URL .

Performance

Note: The model is trained mainly on English.

Model	MMLU	GPQA-diamond	MATH-500	IFEVAL	LIVEBENCH	HUMANEVAL	Average
SmallThinker-21BA3B-Instruct	84.43	55.05	82.4	85.77	60.3	89.63	76.26
Gemma3-12b-it	78.52	34.85	82.4	74.68	44.5	82.93	66.31
Qwen3-14B	84.82	50	84.6	85.21	59.5	88.41	75.42
Qwen3-30BA3B	85.1	44.4	84.4	84.29	58.8	90.24	74.54
Qwen3-8B	81.79	38.89	81.6	83.92	49.5	85.9	70.26
Phi-4-14B	84.58	55.45	80.2	63.22	42.4	87.2	68.84

For the MMLU evaluation, we use a 0-shot CoT setting.

All models are evaluated in non-thinking mode.

Speed

Model	Memory(GiB)	i9 14900	1+13 8ge4	rk3588 (16G)	Raspberry PI 5
SmallThinker 21B+sparse	11.47	30.19	23.03	10.84	6.61
SmallThinker 21B+sparse+limited memory	limit 8G	20.30	15.50	8.56	-
Qwen3 30B A3B	16.20	33.52	20.18	9.07	-
Qwen3 30B A3B+limited memory	limit 8G	10.11	0.18	6.32	-
Gemma 3n E2B	1G, theoretically	36.88	27.06	12.50	6.66
Gemma 3n E4B	2G, theoretically	21.93	16.58	7.37	4.01

Note: i9 14900, 1+13 8ge4 use 4 threads, others use the number of threads that can achieve the maximum speed. All models here have been quantized to q4_0. You can deploy SmallThinker with offloading support using PowerInfer

Model Card

Architecture	Mixture-of-Experts (MoE)
Total Parameters	21B
Activated Parameters	3B
Number of Layers	52
Attention Hidden Dimension	2560
MoE Hidden Dimension (per Expert)	768
Number of Attention Heads	28
Number of KV Heads	4
Number of Experts	64
Selected Experts per Token	6
Vocabulary Size	151,936
Context Length	16K
Attention Mechanism	GQA
Activation Function	ReGLU

How to Run

Transformers

transformers==4.53.3 is required, we are actively working to support the latest version. The following contains a code snippet illustrating how to use the model generate content based on given inputs.

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

path = "PowerInfer/SmallThinker-21BA3B-Instruct"
device = "cuda"

tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(path, torch_dtype=torch.bfloat16, device_map=device, trust_remote_code=True)

messages = [
    {"role": "user", "content": "Give me a short introduction to large language model."},
]
model_inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True).to(device)

model_outputs = model.generate(
    model_inputs,
    do_sample=True,
    max_new_tokens=1024
)

output_token_ids = [
    model_outputs[i][len(model_inputs[i]):] for i in range(len(model_inputs))
]

responses = tokenizer.batch_decode(output_token_ids, skip_special_tokens=True)[0]
print(responses)

ModelScope

ModelScope adopts Python API similar to (though not entirely identical to) Transformers. For basic usage, simply modify the first line of the above code as follows:

from modelscope import AutoModelForCausalLM, AutoTokenizer

Statement

• Due to the constraints of its model size and the limitations of its training data, its responses may contain factual inaccuracies, biases, or outdated information.
• Users bear full responsibility for independently evaluating and verifying the accuracy and appropriateness of all generated content.
• SmallThinker does not possess genuine comprehension or consciousness and cannot express personal opinions or value judgments.