Anbeeld Supercharges Local AI With Beellama.cpp Speed Overhaul

Beellama.cpp is a fork of the popular llama.cpp project that squeezes extra speed and memory efficiency out of local GGUF model inference. It adds DFlash speculative decoding, TurboQuant KV‑cache compression, and reasoning‑loop protection while keeping all the tools that llama.cpp users already know. The result is a drop‑in replacement that can deliver significantly higher token‑generation rates and let you fit longer conversations into the same VRAM.

Anbeeld, the developer, built this fork to help GPU owners push more performance from their existing hardware. The focus is on practical speedups for structured tasks like code generation, boilerplate, and JSON output, where the gains are largest. With the same model and card, you can often see generation speeds several times faster than a standard llama.cpp setup.

DFlash and TurboQuant for faster inference and longer context

The fork is a natural fit for anyone running local LLMs on cards like the RTX 3090 or 4090. It excels when you need to generate large, predictable chunks of code, test stubs, or structured data. The KV‑cache compression options also make it easier to serve long‑context use cases without running out of VRAM.

Developer notes and what’s ahead

Anbeeld points out that DFlash delivers its biggest speed‑ups on low‑entropy content and works best on repetitive patterns; open‑ended creative writing sees smaller improvements. Some advanced features, such as the TCQ cache types, are CUDA‑only, and the DDTree branch verification is still experimental. Prebuilt Windows binaries are available, and building from source with the right flags lets you unlock the fastest performance on your exact machine.

“BeeLlama.cpp (or just Bee) is a performance-focused llama.cpp fork for squeezing more speed and context out of local GGUF inference.” — Source: GitHub