Qwen3-Coder 480B Local Inference: A Deep Dive by KodekX Architects

Qwen3-Coder 480B Local Inference: A Deep Dive by KodekX Architects

When Alibaba dropped the 480B parameter Qwen3-Coder, the first question on Hacker News wasn't "Is it good?" but "Can I actually run this at home?" After spending the last week pushing this model to its limits in our KodekX labs, I can give you a straight answer.

The answer is yes—if you're willing to get your hands dirty and treat RAM bandwidth like gold. This isn't plug-and-play, but for the first time, true large-scale agentic coding is within reach of a prosumer setup. At KodekX, our "Performance First" principle means we're obsessed with optimization. When a model like Qwen3-Coder was released, our architects couldn't resist pushing it to its limits.

What Makes This Model Special (The "Why")

This isn't just another parameter bump. Qwen3-Coder's architecture is fundamentally different, making it uniquely suited for local execution despite its massive size.

Mixture-of-Experts (MoE)

This is the key. You aren't loading all 480B parameters for every single token. The model is a collection of smaller "expert" networks. During inference, it only routes your request through a fraction of them, bringing the active parameter count down to a much more manageable ~35B. You get the knowledge of a huge model with the speed of a smaller one.

Massive Context

It boasts a native 256K context window that can be stretched to 1M tokens with positional interpolation. To put that in perspective, it can read your entire codebase at once. This isn't just about feeding it a long prompt; it's about giving it the full context to perform complex, multi-file refactoring that smaller models can only dream of.

Agentic Training

Qwen3-Coder was trained not just on code, but on using code. It went through extensive reinforcement learning where it had to write code, execute it, read the output (including errors), and then debug its own work. This gives it superior, almost spooky, agentic capabilities right out of the box.

The Hardware Reality Check (The Core)

Let's get down to brass tacks. What do you actually need to run this?

RAM is King, Not VRAM

Forget stacking five RTX 4090s. With MoE models, the full unquantized model weights (~960 GB) must be loaded into your system RAM. Your VRAM is only used to offload layers and process the prompt, but system RAM capacity and bandwidth are the primary bottlenecks. At KodekX, we've optimized our cloud infrastructure to handle these requirements seamlessly.

Quantization Numbers

No one is running the full FP16 version. Quantization is non-negotiable. Here are the real-world file sizes for the most popular `llama.cpp` quants:

• IQ4_XS (4.25 bpw): ~295 GB
• Q4_K_M (4.65 bpw): ~323 GB
• Q5_K_M (5.53 bpw): ~382 GB

Step-by-Step Garage Build (The "How")

Ready to try it? Here's the condensed guide we used in our KodekX labs.

Get the Model

Grab your preferred GGUF quant from the Hugging Face repo. I recommend starting with Q4_K_M.

# Use huggingface-cli to download
huggingface-cli download Qwen/Qwen3-Coder-480B-Chat-GGUF Qwen3-Coder-480B-Chat.Q4_K_M.gguf --local-dir .

(Optional but Recommended) Generate an imatrix

This importance matrix helps llama.cpp make smarter quantization choices, preserving model quality. It takes time but is worth it.

# This will take a while, use a small calibration dataset
./imatrix -m Qwen3-Coder-480B-Chat.Q4_K_M.gguf -f "calibration-data.txt" -o imatrix.dat

Quantize with llama.cpp

If you downloaded a larger quant and want to make it smaller, you can requantize it.

./quantize --imatrix imatrix.dat Qwen3-Coder-480B-Chat.Q5_K_M.gguf Qwen3-Coder-480B-Chat.IQ4_XS.gguf iq4_xs

Run the Server

Start the llama.cpp server, offloading as many layers (-ngl) to your GPU as VRAM allows.

./server -m Qwen3-Coder-480B-Chat.Q4_K_M.gguf -c 256000 --host 0.0.0.0 --port 8080 -ngl 33

Connect Your Editor

Point your favorite tool to the local endpoint. In VS Code's settings.json for a tool like Continue, it would look like this:

"continue.llms": [
  {
    "title": "Local Qwen3",
    "model": "qwen3-coder-480b",
    "apiBase": "http://127.0.0.1:8080"
  }
]

The Takeaway

Deeper Dives from the KodekX Lab

Implementation Notes