Running real-time neural networks on standard consumer computers requires squeezing absolute performance out of local silicon. When dealing with Whisper speech-to-text models on Apple Silicon (M1/M2/M3 chips), developers face a choice: do we execute on standard CPU threads or compile for Apple's proprietary CoreML framework?
"Unified memory architecture means the GPU and CPU don't waste clock cycles copying matrices back and forth. That is local-first's primary superpower."
To identify the optimal configuration for Maple's meeting recorder engine, we conducted a rigorous benchmark suite on an M3 MacBook Air. We measured transcription speed, system heat levels, and active RAM footprints across standard CPU (using neon vector instructions) and CoreML configurations.
Silicon Memory Routing
Figure 1: Apple Silicon Unified Memory Routing
Performance Benchmarks
| Execution Backend | Real-time factor | CPU Utilization | Battery drain rate |
|---|---|---|---|
| CPU (4 threads) | 0.32x | 88% (Heavy system lock) | 12.4% per hour |
| CoreML (Neural Engine) | 0.08x | 4% (Background idle) | 1.8% per hour |
Whisper.cpp CoreML Initialization
#include "whisper.h"
// Initialize Whisper context optimized for CoreML
struct whisper_context_params params = whisper_context_default_params();
params.use_gpu = true; // metal acceleration
params.use_coreml = true; // apple neural engine hook
struct whisper_context * ctx = whisper_init_with_params("models/ggml-base.en-q5.bin", params);