Devs - you have 64gb of VRAM - which model do you use for coding?

Reddit r/LocalLLaMA News

Summary

A developer with 64GB VRAM shares their preference for an unsloth version of Qwen 3.5 122b-a10b for coding and asks the community for their recommendations.

I've currently settled on an unsloth version of Qwen 3.5 122b-a10b model (UD-IQ4_NL). With 100k bf16 context window, I only had to load a few layers into CPU/RAM, it runs around 30 tok/sec which is fine for me. I've tested many models, hours of testing but I am currently deeply impressed with this one. I also use the Qwen 3.6 models (both) depending on need, but I think this biggun' is about to become my daily driver. Curious to know what others with similar VRAM capacity use?
Original Article

Similar Articles

Qwen 35B-A3B is very usable with 12GB of VRAM

Reddit r/LocalLLaMA

A user benchmarks Qwen 35B-A3B (a 35B MoE model) on a 12GB RTX 3060, finding that 12GB VRAM is a practical sweet spot for running the model with 32k context, achieving ~47 t/s generation.

Is anyone getting real coding work done with Qwen3.6-35B-A3B-UD-Q4_K_M on a 32GB Mac in opencode, claude code or similar?

Reddit r/LocalLLaMA

A user shares their experience running Qwen3-35B-A3B quantized model on an M2 MacBook Pro with 32GB RAM for coding tasks via opencode and llama.cpp, finding that the 32K context window limit causes critical memory loss during compaction, making complex coding tasks impractical. They conclude that meaningful agentic coding with this model likely requires at least 128K context, exceeding what their hardware can support.