A user from the LocalLLaMA community reported remarkable results with the Qwen3 Coder Next 8FP model. During a test, the model was tasked with converting the entire Flutter documentation using a prompt of just three sentences, leveraging a context window of 64,000 tokens.

Performance and Hardware Requirements

Running this task required the use of approximately 102GB of RAM, out of a total of 128GB available in the system. The user pointed out how other open-source models, including GPT OSS 120B, GLM 4.7 Flash, SERA 32B, Devstral 2 Small, SEED OSS, and Nemotron 3 Nano, were unable to complete the same operation successfully or showed lower performance.

Additional Considerations

The user also mentioned some issues related to the VS Codium user interface with Cline, particularly in managing the "thinking" windows during model execution, which make scrolling difficult even with 32GB of RAM. This highlights how optimizing the development environment is crucial to fully exploit the capabilities of large language models.