BeeLlama.cpp v0.4.1 enhances KV cache quantization with KVarN and precision tail

BeeLlama.cpp has released version 0.4.1, introducing significant enhancements to KV cache quantization. The update includes KVarN for improved precision per bit with modest performance trade-offs, and KV cache precision tail (KVPT) which allows recent tokens to be stored losslessly while the rest are quantized. Additionally, new quantization types like q6_0, q6_1, q2_0, q2_1, q3_0, and q3_1 have been added to offer more flexibility in balancing precision and VRAM usage. AI

IMPACT Offers more efficient local LLM deployment through advanced KV cache quantization techniques.

RANK_REASON This is a software update for a specific fork of llama.cpp, not a release from a frontier lab.

Read on r/LocalLLaMA →

• BeeLlama.cpp
• KV cache
• KV cache precision tail
• q2_0
• q2_1
• q3_0
• q3_1
• q6_0
• q6_1
• Q8_0
• Qwen 3.6 27B Q5_K_S 64k

• model release
• infra

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