# I Ran a Good Model With Decent Hardware

> I saw a benchmark showing Qwen3.6 outperforming Claude Sonnet on my laptop. I didn't believe it. So I ran it myself — mistakes, PowerShell errors, and all.

Canonical: https://nosistech.com/running-local-ai-gaming-laptop-setup-2/
Published: 2026-04-18
Last updated: 2026-10-26
Author: Carlos Paredes, NosisTech LLC

I saw a benchmark table on X showing a new model called Qwen3.6-35B-A3B outperforming Claude Sonnet-4.5 on almost every vision and reasoning benchmark.

My first reaction was disbelief. My second reaction was to immediately check if I could run it on my laptop.

## **What Is Qwen3.6-35B-A3B and Why Does It Matter**

The name tells you everything once you decode it. 35B means 35 billion total parameters. A3B means only 3 billion are activated per token. This is Mixture of Experts architecture, the model has the knowledge capacity of a 35B model but runs at the compute cost of roughly a 3B model. Released by Alibaba's Qwen team in April 2026, it's a multimodal model handling text, images, and video, specifically designed for agentic coding and reasoning tasks. The benchmark showed it beating Claude Sonnet-4.5 on many things.

### **Step 1 : Downloading the Model**

I already had Ollama installed and Open WebUI running at localhost:8080. I opened PowerShell and ran: ollama run qwen3.6:35b-a3b-q4_K_M. The download started immediately, 23GB total, about 10–15 minutes. When it finished, the CLI prompt appeared and I was talking to the model.

#### **Step 2** : **First Benchmark**

Before touching any settings, I ran the model with the --verbose flag and sent a short prompt. Results: 16.32 tokens/sec eval rate, 45.06 tokens/sec prompt eval, 121ms load time, 2048 default context. 16 tokens per second for a 23GB model on a laptop. I was not expecting that.

##### **Step 3 : Quality Test**

I asked a real question I use professionally: "What is the difference between AI governance and AI compliance? Answer as a senior consultant." The thinking trace showed the model building a delivery framework before writing a single word, deconstructing both concepts, adopting the consultant persona deliberately, self-correcting mid-draft. Then the actual response came: aviation analogy, "checkbox AI" framing, RACI mention, governance-as-floor-not-ceiling positioning, a comparison table, and a three-step recommendation. Good quality response.

###### **Step 4 : Configuring Optimal Settings**

The default context of 2,048 tokens is dangerously low for a model with thinking mode, the thinking trace itself consumes tokens before the answer starts. I needed to configure via Modelfile because Ollama's CLI doesn't support --option flags (Mistake 2) and PowerShell uses backtick not for line continuation (Mistake 1). The Modelfile approach bakes settings permanently into a named model: context window 32,768, temperature 1.0, top_p 0.95, top_k 20, presence_penalty 1.5, repeat_penalty 1.0, max_tokens 8,192. After configuration: 17.32 tokens/sec, faster than default, because baking settings lets Ollama allocate resources more efficiently at load time. I had to test this multiple times to confirm.

###### **Step 5 : One-Click Launcher**

My previous startup required opening Ollama, PowerShell, finding a saved command, copying it, pasting it, then navigating to localhost:8080. I built a .bat file to do it all with one double-click. Three more mistakes along the way: the standard Desktop path doesn't exist when OneDrive is active and Windows is in Spanish (Mistake 3); RAG environment variables caused Open WebUI to crash (Mistake 4); the & background operator is bash syntax and doesn't work in .bat files (Mistake 5). The final working file: start Ollama, wait 10 seconds, launch Open WebUI in its own cmd window, wait 30 seconds, open Chrome to localhost:8080.

###### **What I Actually Learned**

MoE architecture is not a compromise, it's a genuine breakthrough for running capable models on consumer hardware. The thinking mode changes the character of the output significantly. And yes, I made five documented mistakes getting here. Every single one was fixable in under five minutes A 23GB model, launched with a double-click, producing governance consulting output that competes with a frontier cloud model. In April 2026, on consumer hardware. That's where we are.

### Keep reading: Local AI on a gaming laptop

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