Qwen3-30B-A3B-NotaMoeQuant-Int4

Overview

We developed a weight-only quantization method specialized for the Mixture-of-Experts (MoE) architecture, and we release Qwen3-30B-A3B quantized with our algorithm. The quantized weights are packed using an AutoRound-based quantization format.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "nota-ai/Qwen3-30B-A3B-NotaMoEQuant-Int4"

# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

# prepare the model input
prompt = "What is large language model?"
messages = [
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True 
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# conduct text completion
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=100
)

print(tokenizer.decode(generated_ids[0], skip_special_tokens=True))

Performance

	PPL (WikiText2)	MMLU-Pro	AIME25	LiveCodeBench v6	Total TPS (Tokens/Sec.)	Memory (GB)
Qwen3-30B-A3B (BF16)	10.8955	75.47	70.00	55.70	1136.56	58.23
Nota MoEQuant (INT4)	11.3046	74.84	70.00	60.18	1262.21	16.01

Note

• Nota MoEQuant use 8-bit quantization for the gate layer, while all other linear layers are quantized to 4 bits.
• Tokens per sec. (TPS) is measured with 16 requests, using 20,000 tokens for prefill and 20,000 tokens for decoding.
• Memory indicates the allocated GPU memory for model parameters.
• Model evaluations weres conducted using AutoRound==0.8.0 and vLLM==0.12.0.