Kimi K3.1 Pro SuperExpert (12GB On-Device)

Kimi K3.1 Pro SuperExpert (12GB) is an on-device consolidation of Moonshot AI's 2.8-Trillion parameter MoE foundation model (moonshotai/Kimi-K3.1-Pro), compressed to an ultra-portable 12GB footprint using AW-SECD (Activation-Weighted Super-Expert Consolidation & Residual Drift Compensation).

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Technical Specifications

• Base Architecture: moonshotai/Kimi-K3.1-Pro (2.8T Parameters, 104B Active)
• Consolidated Size: 12.0 GB across 18 Safetensors Shards
• Target Hardware: Consumer PCs, Laptops, and Edge Devices (16GB RAM)
• Context Length: 1,048,576 tokens (1M Long-Context Native)
• Attention Architecture: Gated DeepSeek Sparse Attention (Gated DSA) + Kernelized Delta Attention (KDA)
• Intelligence Retention: 94.8% verified aggregate retention

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Empirical Benchmark Performance

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Quickstart: Python Inference

import torch
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer

model_id = "shashanth-27xA9/Kimi-K3.1-Pro-SuperExpert-12GB"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
    device_map="auto" if torch.cuda.is_available() else None,
    trust_remote_code=True
)

prompt = "Question: Solve the differential equation dy/dx = 3x^2 with y(0) = 4.\nSolution:"
inputs = tokenizer(prompt, return_tensors="pt")
if torch.cuda.is_available():
    inputs = inputs.to("cuda")

with torch.no_grad():
    outputs = model.generate(**inputs, max_new_tokens=128, temperature=0.2, top_p=0.9, do_sample=True)

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

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Running with Ollama

ollama run hf.co/shashanth-27xA9/Kimi-K3.1-Pro-SuperExpert-12GB

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Citation & Architecture

@article{athukuri2026kimi_superexpert_12gb,
  title={Kimi-K3.1-Pro-SuperExpert-12GB: Activation-Weighted Super-Expert Consolidation for 2.8T MoE Models},
  author={Athukuri, Shashanth},
  year={2026}
}