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A new implementation of Multi-Token Prediction (MTP) in llama.cpp achieves a 40% speedup for Gemma 4 models, tested on a MacBook Pro M5Max. The post provides links to quantized GGUF models and the patched source code.
A single RTX 3090 pushes 134 tok/s on the fresh 27B Qwen 3.5 Dense and 73 tok/s on Qwen 3.6-27B via fused kernels plus speculative decoding, with GGUF drops the same evening.
Author shares experience hitting diminishing returns with FP16 + ONNX + pruning on 162 MB transformer, seeks advice on next best steps among quantization, distillation, low-rank factorization, or hardware-specific tricks.
User benchmarks Qwen3.6-27B-Q8_0 at ~13 tokens/sec on 3 mixed GPUs with 128k context via llama.cpp, asking if performance is typical.