AI BriefingBeeLlama.cpp: llama.cpp Optimization
·2026.05.10 01:05
Key point
BeeLlama.cpp, a new fork that maximizes the performance of llama.cpp, has been released.
Details
BeeLlama.cpp is a performance-focused fork of llama.cpp developed to maximize the speed and context capacity of GGUF inference.
Its key technical features are as follows:
- DFlash Speculative Decoding: Stores the target model's hidden states in a ring buffer, which the drafter references to perform ultra-fast inference.
- TurboQuant / TCQ KV-cache Compression: Supports KV cache compression of 4x up to 7.5x, improving VRAM efficiency.
- Other features: Includes adaptive draft control, inference loop protection, and multimodal (Vision) support.
On an RTX 3090 environment using the Qwen 3.6 27B Q5 model, it is possible to achieve 2-3x faster speed (up to 135 tps) compared to before while maintaining a 200k context.
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