**VibeASR.cpp** is the official inference runtime for Microsoft's **VibeVoice-ASR-BitNet**, and its whole reason for existing is a single question: can a modern LLM-based speech recognizer run in real time on an ordinary CPU? The repository has drawn **143 stars and 18 forks** since its **23 July 2026** release, with the weights pulling **16,586 downloads and 182 likes** on Hugging Face. The answer it offers is heterogeneous quantization — **I8_S for the VAE tokenizer and I2_S (ternary BitNet) for the language-model decoder** — rather than the uniform int4/int8 treatment most runtimes apply. The size story is the clearest part. Microsoft first swaps the original **Qwen2.5-7B** language model for **Qwen2.5-1.5B**, then quantizes each half differently: the VAE tokenizer drops from **1.31 GB to 0.65 GB** (2.0x) and the LM decoder from **3.32 GB to 0.92 GB** (3.6x), taking the total from **4.62 GB to 1.58 GB** — a **2.9x** compression. The reported accuracy cost of the 7B-to-1.5B substitution is a **1-4% absolute WER increase**, which is the kind of trade the README states plainly rather than burying. Speed comes from custom **SIMD kernels and operator fusion inside ggml**, and the project reports **1.6-2.3x faster inference than Whisper.cpp** at comparable model sizes. The published tables are more specific: on an **AMD EPYC 7V13** the real-time factor is 1.98 single-threaded but **0.77 at three threads and 0.42 at eight**, with the speedup over Whisper.cpp measured at 2.28x at one thread narrowing to 1.55x at eight. An **Apple M4** hits **RTF 0.68 at two threads**, and an **Intel Core i7-13700** reaches **0.97 at two threads**. Anything under 1.0 is faster than real time, so the practical claim is that a two-to-four-thread consumer CPU is enough. On accuracy the picture is mixed and worth reading carefully. The BitNet build posts **8.25 WER on MLC-EN** against Parakeet's 8.40, Whisper's 13.57, SenseVoice's 12.39 and FunASR's 11.36, and it leads on the harder meeting sets — **21.36 on AMI-ihm** and **25.87 on AMI-sdm**, versus 27.07 and 36.92 for Whisper. It loses on clean read speech: **2.41 on LibriSpeech clean** where Parakeet manages 1.49, and **5.21 on Fleurs-en** where Whisper reaches 3.99. Multilingual coverage spans MLC French, Italian, Korean, Portuguese and Vietnamese, though WER there runs from **11.15 (Korean) to 24.87 (Portuguese)**. The README also carries an explicit caveat that these are standard-accent corpora and that accented or dialectal speech may degrade further. Setup is deliberately small: **Python 3.9+, CMake 3.14+, a C++11 GCC or Clang toolchain, and about 2 GB of disk** for code plus the pre-quantized GGUF files (`vibeasr-vae-encoder-i8_s.gguf` and `vibeasr-lm-i2_s-embed-q6_k.gguf`). A `setup_env.py` one-command path, an `asr_infer` CLI and a Gradio web demo are all included. The one real friction point is Windows: **MSVC is rejected outright by the build** and MinGW-w64 GCC or Clang is required, which is a meaningful obstacle for the CPU-first Windows audience this project would otherwise serve well. Licensing is **MIT** and a technical report is published at arXiv:2607.21075.