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Behnam Ebrahimi edited this page Mar 29, 2026 · 2 revisions

Comparison

Vayu vs Other Whisper Implementations

Feature Vayu whisper.cpp faster-whisper OpenAI Whisper
Platform macOS (Apple Silicon) Cross-platform Cross-platform Cross-platform
Backend MLX GGML/Metal/CUDA CTranslate2/CUDA PyTorch
Speed (Apple Silicon) 3-5x faster ~2x faster N/A (CPU only on Mac) Baseline
Speed (NVIDIA GPU) N/A ~2x faster 4-5x faster Baseline
Language Python C/C++ Python Python
Install uv pip install / pip install Build from source pip install pip install
Python API Yes Via bindings Yes Yes
CLI Yes Yes No Yes
Word timestamps Yes Yes Yes Yes
Batched decoding Yes No Yes No
Quantization 4-bit, 8-bit 4-bit, 5-bit, 8-bit 8-bit, 16-bit No
Speculative decoding Yes (experimental) No No No
Output formats txt, srt, vtt, tsv, json txt, srt, vtt, csv Custom txt, srt, vtt, tsv, json
Models All Whisper + distil All Whisper All Whisper + distil All Whisper

When to Use Vayu

Choose Vayu if:

  • You have an Apple Silicon Mac (M1/M2/M3/M4)
  • You want the fastest possible transcription on macOS
  • You need a simple pip install / uv pip install + Python API
  • You want batched decoding for throughput

Choose whisper.cpp if:

  • You need cross-platform support (Windows/Linux/Mac)
  • You want minimal dependencies (pure C++)
  • You're deploying on edge devices or embedded systems
  • You need CoreML or Metal support on Mac

Choose faster-whisper if:

  • You have an NVIDIA GPU
  • You need the fastest transcription on Linux/Windows
  • You want a Python API with CTranslate2 optimization

Choose OpenAI Whisper if:

  • You want the reference implementation
  • You need compatibility with existing Whisper code
  • Platform performance isn't a priority

Speed Positioning

On Apple Silicon Macs:

Fastest ──────────────────────────── Slowest

Vayu (batched)  >  whisper.cpp  >  OpenAI Whisper
   3-5x               ~2x              1x

On NVIDIA GPUs:

Fastest ──────────────────────────── Slowest

faster-whisper  >  whisper.cpp  >  OpenAI Whisper
    4-5x              ~2x              1x

Key Differentiators

Batched Decoding

Vayu's core innovation. Processes multiple 30-second audio segments in a single forward pass instead of one at a time. This is only efficient on hardware with high parallel compute — Apple Silicon's unified memory architecture makes it particularly effective.

MLX Native

Built directly on Apple's MLX framework, which is optimized for Apple Silicon's unified memory, Neural Engine, and GPU. No translation layers or compatibility shims.

Speculative Decoding

Unique to Vayu. Uses a small model (e.g., tiny) to draft tokens, verified by a large model (e.g., large-v3) in a single pass. Potential 2-3x additional speedup when draft model is accurate.

Simplicity

One-line install, simple Python API (LightningWhisperMLX), and full-featured CLI. No need to compile from source or configure GPU drivers.

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