Tired of relying on the cloud? I tested 20+ local LLMs on my machine to find the best coding assistant for Python. After a hardware upgrade and a new wave of model releases, the results have changed. My champion has a new name.
The Allure of Local AI
There’s something incredibly powerful about running an AI model directly on your own machine. No latency, no privacy concerns, no subscription fees—just raw, uninterrupted coding power. As a developer who lives in Python, I’ve been on a quest to find the perfect local coding companion. I didn’t want a jack-of-all-trades; I wanted a master of one: code generation and explanation.
My testing rig isn’t a supercomputer anymore; it’s a dedicated workstation. This makes my results perfect for other developers with similarly powerful, but not unlimited, setups. My local Coding Assistant runs on Windows 11 with the following hardware:
- CPU: Ryzen 9 5900X
- GPU: NVIDIA ROG Strix 2080TI – 11 GB VRAM
- RAM: 32 GB (with 32 GB of page file/swap)
- DISK: 1 TB SD & 1/2 TB SATA and 12 TB (NAS)
As you can see, the 11GB VRAM on my 2080TI is the real bottleneck. It means I can’t just run the biggest 70B models comfortably. I need something that fits in that 11 GB envelope and flies.
The New Challenger Arrives
For a long time, my go-to was `deepseek-coder-v2:16b`. It was brilliant—a perfect balance of speed and intelligence. But in this space, standing still means falling behind. The “best” model is a moving target. Recently, I started hearing whispers about a new variant: **Qwen3-Coder-Next**.
Intrigued, I pulled down two versions to test:
- Ollama Library: qwen3-coder-next:q4_K_M` , ~52 GB , The official Ollama 4-bit version.
- Ollama (Community): bazobehram/qwen3-coder-next , ~48 GB , A community quant using Unsloth, rumored to have better quality for the size.
Now, you might be thinking: “52GB? That’s huge! How does it fit in 11GB of VRAM?” And you’d be right to ask. It doesn’t. It barely fits in my 32GB of system RAM. Because of my hardware limits, I had to get creative. This is a crucial point for anyone else with a similar setup:
Conservative Settings:
I set my environment variables to be very conservative:
- OLLAMA_NUM_PARALLEL=1
- OLLAMA_MAX_LOADED_MODELS=1
- OLLAMA_KEEP_ALIVE=0
to prevent Ollama from trying to use too much memory at once.
Small Context: I run with a limited context window (`num_ctx 4096`). This keeps the memory usage manageable. CPU Inference: At this size, the model runs almost entirely in system RAM on the CPU. It’s not as fast as a GPU-powered model, but the *quality* of the output makes the slightly slower token generation completely worth it. It’s a trade-off of speed for intelligence.
I use the Reins application on my MacBooks to connect to this beast running on my Windows machine, turning my whole network into a private AI coding cluster.
Why Qwen3-Coder-Next Won Me Over
After spending weeks with it, the `qwen3-coder-next` models have completely replaced DeepSeek-Coder as my daily driver. Here’s why the switch was a no-brainer:
1. Unmatched “Intelligence” for its Class
This model is smart. It handles complex, multi-step instructions with a coherence that I haven’t seen from other models in this size range. When I ask it to refactor a messy function, it doesn’t just make it work; it makes it elegant, using the latest Python features appropriately. It feels like the knowledge of a 70B model distilled into a (still large) 50GB package.
2. Next-Level Code Generation
Its understanding of context is incredible. It can look at a few hundred lines of my existing code in the chat history and generate new functions that perfectly match my style and project patterns. It’s less like an autocomplete and more like a pair programmer who just “gets” what you’re trying to build.
3. The Unsloth Advantage
I tested both versions, and the community report seems true. The `bazobehram/qwen3-coder-next` model, quantized with Unsloth, feels marginally more coherent and creative than the official Ollama `q4_K_M` version, despite being slightly smaller. It’s my current pick.
How It Stacks Up
DeepSeek-Coder-V2 (my old champ): DeepSeek is still a fantastic, fast, and efficient model. But Qwen3-Coder-Next is a significant step up in raw reasoning power and code quality. It’s like moving from a reliable sedan to a luxury sports car. You feel the difference immediately.
70B Giants (like Llama 3.3): I can’t run them. But from what I’ve seen online, Qwen3-Coder punches well above its weight. I’m getting close to “giant” performance without needing the 48GB of RAM those models demand. Smaller Specialists (like Phi-4):** The smaller models are great for quick autocomplete, but they fall apart on complex architectural tasks. Qwen3 handles both the quick snippets and the big-picture design.
My Verdict and Recommendation
If you are a Python developer with a decent CPU and at least 32GB of system RAM, and you’re willing to prioritize intelligence over raw token-spewing speed, **qwen3-coder-next is the current king of the hill.** It is, without a doubt, the smartest coding assistant I’ve ever run locally.
Yes, it requires a bit more patience and some tweaking of settings, but the result is a private, powerful, and deeply insightful AI partner that has fundamentally changed how I code.
Ready to try it? It’s easy to get started:
bash
# For the official version (larger)
ollama pull qwen3-coder-next:q4_K_M
# Or for the community Unsloth version (my recommendation)
ollama pull bazobehram/qwen3-coder-next
ollama run bazobehram/qwen3-coder-next
I’d love to hear about your experiences. Have you found a different model that works better for your use case? Let me know in the comments