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. 🙂 submitted by /u/Elemental_Particle [link] [comments]

Source: r/LocalLLaMA | 2026-08-24

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