AI tools-ai Jun 8, 2026 1 min read

llama.cpp b9235: Accelerating Inference with Speculative N-gram Tuning

The llama.cpp b9235 release introduces Speculative N-gram Tuning, significantly optimizing decode speeds when running large models like Qwen3.6 27B.

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Llamacpp GPU Inference Optimization Open Source Qwen

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The latest release of llama.cpp (b9235) introduces new tools designed to boost inference speeds. Most notably, the Speculative N-gram Tuning method has been successfully tested on the RTX 5090 GPU.

Key Developments

Tests with the Qwen3.6 27B model over 10,000 tokens show that increasing the n-gram map size (--spec-ngram-map-k4v-size-m) significantly improves decode throughput. This technique utilizes n-gram-based prediction to accelerate token generation without compromising the accuracy of the base model.

Why It Matters

llama.cpp is a core tool for running local AI in Vietnam. Optimizing throughput on consumer graphics cards (as well as high-end ones like the RTX 5090) enables chatbot and agent applications to run more smoothly, reducing response latency in long-text processing tasks.