 | I am hearing a lot about many models smaller fine tuned models that are pulling above their weight and people are also claiming that those models perform much better than Qwen3.5 35B. I agree that some smaller fine-tuned models, and certainly larger models, are great. But I want to share my experience where Qwen3.5 35B MOE has really surprised me. Here are some snippets i have attached that explain more: Model: Qwen3.5-35B-A3B-GGUF\Qwen3.5-35B-A3B-UD-Q4_K_L.gguf What was tested research paper i used: https://arxiv.org/html/2601.00063v1 [link] [comments] |
Qwen3.5 35b is sure one the best local model (pulling above its weight)
Reddit r/LocalLLaMA / 3/15/2026
💬 OpinionDeveloper Stack & InfrastructureTools & Practical UsageModels & Research
Key Points
- The Reddit post argues that Qwen3.5-35B MOE is one of the best local models, capable of pulling above its weight compared with smaller fine-tuned models.
- The author shares a concrete test setup (llama-server with reasoning disabled and --fit on, Qwen3.5-35B-A3B-GGUF, CLI Qwen-code, RTX 5080 Mobile, context 70K, PP 373, TG 53.57) to demonstrate its performance.
- The tester used the model to design a visual app with interactive visualizations for a research paper and to generate a web app inspired by another large React app, referencing an arXiv paper (2601.00063v1).
- The post links a Reddit gallery and comments, highlighting practical, real-world usage of local LLMs rather than just benchmarks.
- Overall, the entry showcases the practical viability of local LLMs for building interactive applications and demos.
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