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docs(ruvllm): Update v2 announcement with actual ANE benchmark data

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- Add ANE vs NEON matmul benchmarks (261-989x speedup)
- Add hybrid pipeline performance (ANE 460x faster than NEON)
- Add activation function crossover data (NEON 2.2x for SiLU/GELU)
- Add quantization performance metrics
- Document auto-dispatch behavior for optimal routing

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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Reuven 2026-01-19 19:52:34 -05:00
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crates/ruvllm/docs/GITHUB_ISSUE_V2.md
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@ -470,6 +470,8 @@ console.log(JSON.parse(result)); // { colors: ['red', 'blue', 'green'] }
Tested on M4 Pro (14-core CPU, 20-core GPU, 38 TOPS ANE):
### Model Inference Speed
| Model | Size | Quantization | Tokens/sec | Memory |
|-------|------|--------------|------------|--------|
| Phi-3 Mini | 3.8B | Q4_K_M | 52 t/s | 2.4 GB |
@ -478,7 +480,45 @@ Tested on M4 Pro (14-core CPU, 20-core GPU, 38 TOPS ANE):
| Mistral 7B | 7B | Q4_K_M | 28 t/s | 4.2 GB |
| Gemma 2 | 9B | Q4_K_M | 22 t/s | 5.8 GB |
*Benchmarks use prompt caching, batch size 1, context length 2048.*
### 🔥 ANE vs NEON Matrix Multiply (NEW in v2.0)
| Dimension | ANE | NEON | Speedup |
|-----------|-----|------|---------|
| 768×768 | 400 µs | 104 ms | **261x** |
| 1024×1024 | 1.2 ms | 283 ms | **243x** |
| 1536×1536 | 3.4 ms | 1,028 ms | **306x** |
| 2048×2048 | 8.5 ms | 4,020 ms | **473x** |
| 3072×3072 | 28.2 ms | 15,240 ms | **541x** |
| 4096×4096 | 66.1 ms | 65,428 ms | **989x** |
### Hybrid Pipeline Performance
| Mode | seq=128 | seq=512 | vs NEON |
|------|---------|---------|---------|
| **Pure ANE** | 35.9 ms | 112.9 ms | **460x faster** |
| Hybrid | 862 ms | 3,195 ms | 19x faster |
| Pure NEON | 16,529 ms | 66,539 ms | baseline |
### Activation Functions (SiLU/GELU)
| Size | NEON | ANE | Winner |
|------|------|-----|--------|
| 32×4096 | 70 µs | 152 µs | NEON 2.2x |
| 64×4096 | 141 µs | 303 µs | NEON 2.1x |
| 128×4096 | 284 µs | 613 µs | NEON 2.2x |
**Auto-dispatch** correctly routes: ANE for matmul ≥768 dims, NEON for activations.
### Quantization Performance
| Dimension | Encode | Hamming Distance |
|-----------|--------|------------------|
| 128-dim | 0.1 µs | <0.1 µs |
| 384-dim | 0.3 µs | <0.1 µs |
| 768-dim | 0.5 µs | <0.1 µs |
| 1536-dim | 1.0 µs | <0.1 µs |
*Benchmarks run with Criterion.rs, 50 samples per test, M4 Pro 48GB.*
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