What's the best local model you've found for 8 GB of VRAM?

I'm curious what other people are using for local LLM coding / agentic coding with only 8 GB of VRAM.

My current setup is:

• Intel Core i7-11800H
• RTX 3070 Laptop, 8 GB VRAM
• 32 GB DDR4 RAM
• openSUSE Tumbleweed / KDE
• Unsloth Studio
• pi.dev as the coding agent

After testing quite a few different models and quantizations, I've currently settled on:

Qwen3.6-35B-A3B-UD-IQ4_NL by Unsloth

For agentic coding tasks, I'm getting roughly 25 generated tokens/s on average, and more importantly, I'm getting surprisingly good final results. The model is generally capable of navigating a codebase, using tools, making changes, and completing reasonably complex tasks without requiring constant intervention.

For me, the important metric isn't simply maximum tokens/sec. I'm looking for the best combination of:

speed + coding ability + tool use + reliability + quality of the final result.

I've tried several other models before settling on Qwen3.6, but so far this has given me the best overall time/performance/result trade-off on my particular hardware.

That said, I obviously haven't tested everything available.

So I'm curious:

If you have an 8 GB VRAM GPU, what local model are you currently using for coding or agentic coding?

And especially: what are your actual tokens/s and how good are the results?

I'm less interested in benchmark scores and more interested in real-world experience: “I gave it this kind of coding task and it actually managed to finish it.”

I'd love to hear what I'm missing before I decide that I've found my personal sweet spot. 🙂