How many of you tried BeeLlama.cpp? How's it? Agentic coding possible with 8GB VRAM?
Summary
The article discusses BeeLlama.cpp, a fork offering advanced DFlash and TurboQuant features, and seeks community feedback on its performance for agentic coding on hardware with limited VRAM.
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BeeLlama.cpp: advanced DFlash & TurboQuant with support of reasoning and vision. Qwen 3.6 27B Q5 with 200k context on 3090, 2-3x faster than baseline (peak 135 tps!)
BeeLlama.cpp is a performance-focused fork of llama.cpp that introduces DFlash speculative decoding and TurboQuant KV-cache compression, enabling high-speed local inference of large models like Qwen 3.6 27B on consumer hardware.
@Anbeeld: BeeLlama v0.4.7 is out! You can now save gigabytes of VRAM when using MTP and DFlash! In the example shown in the image…
BeeLlama v0.4.7 is released, offering gigabytes of VRAM savings through independent drafter ubatch sizing for MTP and DFlash, along with major ROCm and CUDA performance enhancements for AI inference.
@NFTCPS: Attention to those running large models locally! Someone has transformed llama.cpp into a performance beast — BeeLlama.cpp. With the same VRAM, inference speed triples and context capacity expands 7.5x. This isn't a slide deck; it's real benchmark data. It stuffs three top-tier optimizations into one codebase: DFlash speculative decoding…
BeeLlama.cpp is a fork of llama.cpp that integrates DFlash speculative decoding, TurboQuant/TCQ KV-cache compression, and adaptive draft control, achieving up to 3x faster inference and 7.5x context expansion on the same hardware.
Local LLM autocomplete + agentic coding on a single 16GB GPU + 64GB RAM
A technical guide on setting up local LLM autocomplete (Qwen2.5-Coder-7B) and agentic coding (Qwen3.6-35B-A3B) on a single 16GB GPU with 64GB+ RAM using llama.cpp, including commands and performance benchmarks.
I tested all llama.cpp's speculative decoding methods on Qwen 3.6 27B: MTP ~2.7x, DFlash ~3.7x, n-gram stack ~6x on real coding. Local AI win. My findings on RTX 6000 PRO.
Comprehensive benchmarks of llama.cpp's speculative decoding methods on Qwen 3.6 27B show n-gram stacking on DFlash achieves up to 6x speedup on iterative coding tasks, with ngram-mod providing most of the gain and zero VRAM cost.