Qwen3.5

Run Alibaba Qwen3.5 on Clore.ai — the freshest frontier model (Feb 2026)

Qwen3.5, released February 16, 2026, is Alibaba's latest flagship model and one of the hottest open-source releases of 2026. The 397B MoE flagship beat Claude 4.5 Opus on the HMMT math benchmark, while the smaller 35B dense model fits on a single RTX 4090. All models come with agentic capabilities (tool use, function calling, autonomous task execution) and multimodal understanding out of the box.

Key Features

• Three sizes: 9B (dense), 35B (dense), 397B (MoE) — something for every GPU

• Beat Claude 4.5 Opus on HMMT math benchmark

• Natively multimodal: Text + image understanding

• Agentic capabilities: Tool use, function calling, autonomous workflows

• 128K context window: Handle large documents and codebases

• Apache 2.0 license: Full commercial use, no restrictions

Model Variants

Model	Params	Type	VRAM (Q4)	VRAM (FP16)	Strength
Qwen3.5-9B	9B	Dense	6GB	18GB	Fast, efficient
Qwen3.5-35B	35B	Dense	22GB	70GB	Best single-GPU
Qwen3.5-397B	397B	MoE	~100GB	400GB+	Frontier-class

Requirements

Component	9B (Q4)	35B (Q4)	397B (multi-GPU)
GPU	RTX 3080 10GB	RTX 4090 24GB	4× H100 80GB
VRAM	8GB	22GB	320GB+
RAM	16GB	32GB	128GB
Disk	15GB	30GB	250GB

Recommended Clore.ai GPU: RTX 4090 24GB (~$0.5–2/day) for 35B — best quality per dollar

Quick Start with Ollama

# Install Ollama
curl -fsSL https://ollama.com/install.sh | sh

# 9B — runs on anything (8GB VRAM)
ollama run qwen3.5:9b

# 35B quantized — needs RTX 4090 (24GB)
ollama run qwen3.5:35b

# As API server
ollama serve &
curl http://localhost:11434/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen3.5:35b",
    "messages": [{"role": "user", "content": "Solve this: if f(x) = x^3 - 3x + 1, find all real roots"}]
  }'

vLLM Setup (Production)

pip install vllm

# 35B on single GPU
vllm serve Qwen/Qwen3.5-35B-Instruct \
  --max-model-len 32768 \
  --gpu-memory-utilization 0.90

# 9B with long context
vllm serve Qwen/Qwen3.5-9B-Instruct \
  --max-model-len 65536

# 397B on multi-GPU cluster
vllm serve Qwen/Qwen3.5-397B-A45B-Instruct \
  --tensor-parallel-size 8 \
  --max-model-len 32768

HuggingFace Transformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "Qwen/Qwen3.5-35B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    load_in_4bit=True  # Fits 35B on 24GB
)

messages = [
    {"role": "system", "content": "You are a helpful math tutor."},
    {"role": "user", "content": "Prove that the square root of 2 is irrational."}
]

input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
output = model.generate(input_ids, max_new_tokens=2048, temperature=0.7, do_sample=True)
print(tokenizer.decode(output[0][input_ids.shape[-1]:], skip_special_tokens=True))

Agentic / Tool Use Example

import json
from openai import OpenAI

client = OpenAI(base_url="http://localhost:11434/v1", api_key="ollama")

tools = [{
    "type": "function",
    "function": {
        "name": "get_gpu_price",
        "description": "Get current rental price for a GPU model on Clore.ai",
        "parameters": {
            "type": "object",
            "properties": {
                "gpu_model": {"type": "string", "description": "GPU model name, e.g. RTX 4090"}
            },
            "required": ["gpu_model"]
        }
    }
}]

response = client.chat.completions.create(
    model="qwen3.5:35b",
    messages=[{"role": "user", "content": "What's the cheapest GPU I can rent for running a 7B model?"}],
    tools=tools,
    tool_choice="auto"
)

# Qwen3.5 will call get_gpu_price with appropriate parameters
print(response.choices[0].message)

Why Qwen3.5 on Clore.ai?

The 35B model is arguably the best model you can run on a single RTX 4090:

• Beats Llama 4 Scout on math and reasoning

• Beats Gemma 3 27B on agentic tasks

• Tool use / function calling works out of the box

• Apache 2.0 = no license headaches

At $0.5–2/day for an RTX 4090, you get frontier-class AI for the cost of a coffee.

Tips for Clore.ai Users

• 35B is the sweet spot: Fits on RTX 4090 Q4, outperforms most 70B models

• 9B for budget: Even RTX 3060 ($0.15/day) runs the 9B model well

• Use Ollama for quick start: One command to serve; OpenAI-compatible API included

• Agentic workflows: Qwen3.5 excels at tool use — combine with function calling for automation

• Fresh model = less cached: First download takes time (~20GB for 35B). Pre-pull before your workload starts

Troubleshooting

Issue	Solution
35B OOM on 24GB	Use load_in_4bit=True or reduce --max-model-len
Ollama model not found	Update Ollama: curl -fsSL https://ollama.com/install.sh | sh
Slow on first request	Model loading takes 30-60s; subsequent requests are fast
Tool calls not working	Ensure you pass tools parameter; use instruct variant only

Further Reading

• Qwen Blog

• HuggingFace Models

• Ollama Library