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The example below is using prompt-based speculative decoding. Specifically, ngram hashing is utilized to suggest drafts of up to 64 tokens. The hasher keeps track of ngrams in the observed contexts, so mostly effective for coding tasks. Here is another demo: https://t.co/LXkWNO3GaR
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"text": "Let me demonstrate the true power of llama.cpp:\n\n- Running on Mac Studio M2 Ultra (3 years old)\n- Gemma 4 26B A4B Q8_0 (full quality)\n- Built-in WebUI (ships with llama.cpp)\n- MCP support out of the box (web-search, HF, github, etc.)\n- Prompt speculative decoding\n\nThe result: 300t/s\n\n(realtime video)",
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