Qwen3.6-35B-A3B

Compare transformer-based models and mixture-of-experts models
in terms of scalability and efficiency.

Transformer models scale by increasing parameter count and compute,
while MoE models improve efficiency by activating only a subset of
parameters per token...

MoE advantages:
1. Higher parameter capacity with lower compute cost
2. Better scalability across distributed systems
3. Improved efficiency for large-scale inference workloads

Design a scalable distributed task queue system using Python
and explain key components.

A scalable distributed task queue system can be built using components
such as a message broker (Redis/Kafka), worker nodes, and a task scheduler...

Key components:
- Producer: submits tasks
- Broker: queues tasks
- Worker: executes tasks
- Result backend: stores results

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://ap-1.aieev.cloud/endpoints/qwen3-6-35b-a3b/v1"
)

response = client.chat.completions.create(
    model="qwen/qwen3.6-35b-a3b",
    messages=[{"role": "user", "content": "Hello!"}]
)

print(response.choices[0].message.content)

curl https://ap-1.aieev.cloud/endpoints/qwen3-6-35b-a3b/v1/chat/completions \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen/qwen3.6-35b-a3b",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: "YOUR_API_KEY",
  baseURL: "https://ap-1.aieev.cloud/endpoints/qwen3-6-35b-a3b/v1",
});

const response = await client.chat.completions.create({
  model: "qwen/qwen3.6-35b-a3b",
  messages: [{ role: "user", content: "Hello!" }],
});

console.log(response.choices[0].message.content);