– **Qwen3.6 27B Model**: A British AI enthusiast successfully reduced Qwen3.6 27B model size from its original 16GB VRAM requirement to fit within a 16GB VRAM NVIDIA RTX 5060 Ti, achieving a token generation speed of 40 tokens per second (tok/s) with the quantized version.
– **Model Versions**: They experimented with two versions: one marked as MTP (15.4 GB) and another non-MTP (15.1 GB). The MTP version demonstrated higher prompt processing speeds at 195 tok/s, while the non-MTP version showed better token generation speed at 24 tok/s.
– **Takeaways**: This experiment highlights the potential for reducing large LLM models to fit into the available VRAM of current hardware. It also underscores the importance of model quantization in achieving these size reductions without sacrificing functionality or performance, making it a significant advancement for deployment on devices with limited memory.
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- The Qwen3.6 27B model was successfully reduced to fit within a 16GB VRAM NVIDIA RTX 5060 Ti.
- Two versions of the quantized model were tested: MTP and non-MTP, with different token generation speeds observed.
- This experiment demonstrates how large language models can be optimized for deployment on devices with limited memory.
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