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The idea is to implement/perform this speculative-free post-training method for popular ggml-optimized LLM models (e.g. Qwen3.8-27B).
Alternatively, when such post-trained models become available, implement support for their diffusion-augmented variants in llama.cpp / ggml.
This will possibly accelerate inference with a small memory footprint, enabling inference of popular LLM models in smaller GPUs.
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The idea is to implement/perform this speculative-free post-training method for popular ggml-optimized LLM models (e.g. Qwen3.8-27B).
Alternatively, when such post-trained models become available, implement support for their diffusion-augmented variants in llama.cpp / ggml.
This will possibly accelerate inference with a small memory footprint, enabling inference of popular LLM models in smaller GPUs.
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