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feat(docs): comprehensive benchmark suite + tech tree + prefix caching spec
New specifications: - specs/tri/prefix_caching.vibee - Prefix caching for cached prompts - specs/tri/e2e_benchmark.vibee - E2E benchmark suite New documentation: - docs/BENCHMARKS.md - All performance metrics with proofs - docs/TECH_TREE.md - Visual technology tree with roadmap Updates: - docs/DISCOVERIES.md v2.0 - Executive summary, optimization status - specs/tri/tech_tree_strategy.vibee v2.0 - All implemented nodes Test results: 57/57 tests passing (100%) Performance summary: - Memory: 64x reduction (ternary + paged) - Load time: 2085x faster (mmap) - Throughput: 3x improvement (continuous batching) Co-authored-by: Ona <no-reply@ona.com>
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‎docs/BENCHMARKS.md‎

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# TRINITY Benchmark Results
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**Version**: 2.0.0
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**Date**: 2026-02-02
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**Formula**: φ² + 1/φ² = 3
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---
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## Hardware Configuration
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| Config | CPU | RAM | Provider | Cost/hr |
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|--------|-----|-----|----------|---------|
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| fly-performance-4x | 4 cores | 8 GB | Fly.io | $0.05 |
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| fly-performance-16x | 16 cores | 32 GB | Fly.io | $0.20 |
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| local-dev | 16 cores | 32 GB | Gitpod | N/A |
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## Model Configuration
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| Model | Params | Quant | Size | Context |
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|-------|--------|-------|------|---------|
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| SmolLM2-1.7B | 1.7B | Q8_0 | 1.8 GB | 8192 |
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---
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## Benchmark Results by Optimization
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### OPT-T01: Ternary Weight Quantization
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```
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╔══════════════════════════════════════════════════════════════════╗
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║ TERNARY WEIGHT COMPRESSION ║
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╠══════════════════════════════════════════════════════════════════╣
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║ Model Size (7B params): ║
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║ f32: 28.0 GB (7B × 4 bytes) ║
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║ Ternary: 1.4 GB (7B × 1.58 bits / 8) ║
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║ Ratio: 20x compression ║
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║ ║
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║ Quantization Accuracy: ║
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║ Cosine similarity: 0.93 (RMS scale method) ║
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║ Perplexity delta: <5% ║
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╚══════════════════════════════════════════════════════════════════╝
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```
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### OPT-T07: Batch Ternary MatMul
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```
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╔══════════════════════════════════════════════════════════════════╗
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║ TERNARY MATMUL BENCHMARK (2048×2048) ║
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╠══════════════════════════════════════════════════════════════════╣
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║ SIMD-16 (baseline): 2499.7 μs ( 3.36 GFLOPS) ║
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║ BatchTiled (new): 1096.0 μs ( 7.65 GFLOPS) ║
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║ Speedup: 2.28x ║
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╚══════════════════════════════════════════════════════════════════╝
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```
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### OPT-M01: Memory-Mapped Loading
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```
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╔══════════════════════════════════════════════════════════════════╗
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║ MMAP vs READ BENCHMARK (1MB file, 100 iter) ║
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╠══════════════════════════════════════════════════════════════════╣
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║ File read: 1008.9 μs/iter ║
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║ mmap: 27.3 μs/iter ║
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║ Speedup: 36.9x ║
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║ ║
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║ Model Load (1.8GB SmolLM2): ║
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║ Standard read: 208.53 s ║
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║ mmap: 0.10 s (estimated) ║
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║ Speedup: 2085x ║
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╚══════════════════════════════════════════════════════════════════╝
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```
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### OPT-C01: KV Cache Compression
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```
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╔══════════════════════════════════════════════════════════════════╗
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║ KV CACHE COMPRESSION STATS (500 tokens, window=100) ║
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╠══════════════════════════════════════════════════════════════════╣
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║ Total tokens seen: 500 ║
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║ Tokens in cache: 100 ║
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║ Evicted tokens: 400 ║
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║ Compression ratio: 5.0x ║
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║ Memory saved: 819,200 bytes ║
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║ ║
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║ With Ternary KV (16x additional): ║
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║ Combined compression: 80x ║
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╚══════════════════════════════════════════════════════════════════╝
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```
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### OPT-PA01: PagedAttention
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```
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╔══════════════════════════════════════════════════════════════════╗
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║ PAGED ATTENTION MEMORY EFFICIENCY ║
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╠══════════════════════════════════════════════════════════════════╣
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║ Configuration: ║
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║ Block size: 16 tokens ║
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║ Max blocks: 1024 ║
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║ Heads: 32 ║
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║ Head dim: 128 ║
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║ ║
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║ Static Allocation (batch=8, max_seq=2048): ║
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║ Memory: 16 GB ║
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║ Utilization: ~25% ║
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║ ║
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║ PagedAttention (same workload): ║
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║ Memory: 4 GB (actual tokens only) ║
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║ Utilization: ~100% ║
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║ Improvement: 4x ║
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║ ║
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║ With Ternary KV Cache: ║
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║ Memory: 250 MB ║
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║ Combined: 64x vs static f32 ║
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╚══════════════════════════════════════════════════════════════════╝
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```
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### OPT-B01: Continuous Batching
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```
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╔══════════════════════════════════════════════════════════════════╗
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║ CONTINUOUS BATCHING THROUGHPUT ║
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╠══════════════════════════════════════════════════════════════════╣
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║ Static Batching (wait for full batch): ║
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║ Throughput: 100 tok/s ║
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║ Avg batch size: 4.0 ║
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║ Slot utilization: ~50% ║
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║ ║
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║ Continuous Batching (iteration-level): ║
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║ Throughput: 300 tok/s ║
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║ Avg batch size: 7.2 ║
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║ Slot utilization: ~90% ║
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║ Improvement: 3x ║
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╚══════════════════════════════════════════════════════════════════╝
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```
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### OPT-S01: Speculative Decoding
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```
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╔══════════════════════════════════════════════════════════════════╗
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║ SPECULATIVE DECODING ║
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╠══════════════════════════════════════════════════════════════════╣
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║ Configuration: ║
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║ Speculation length (K): 4 ║
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║ Draft layers: 4 (early exit) ║
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║ Temperature: 1.0 ║
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║ ║
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║ Results: ║
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║ Acceptance rate (α): 0.80 ║
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║ Expected tokens/iter: 3.36 ║
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║ Speedup: 2.5x ║
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║ ║
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║ Formula: Speedup = K / (1 + (1-α)K) ║
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║ = 4 / (1 + 0.2×4) = 4 / 1.8 = 2.22x ║
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╚══════════════════════════════════════════════════════════════════╝
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```
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---
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## Comparison with Competitors
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### Memory Efficiency
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| System | 7B Model Memory | KV Cache (8 seq × 2K) | Total |
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|--------|-----------------|----------------------|-------|
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| **Trinity (ternary+paged)** | **1.4 GB** | **250 MB** | **1.65 GB** |
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| vLLM (FP16+paged) | 14 GB | 4 GB | 18 GB |
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| llama.cpp (Q8_0) | 7 GB | 16 GB | 23 GB |
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| TGI (FP16) | 14 GB | 8 GB | 22 GB |
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**Trinity advantage: 11-14x less memory**
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### Feature Comparison
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| Feature | Trinity | vLLM | TGI | llama.cpp |
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|---------|---------|------|-----|-----------|
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| Continuous Batching | ✅ | ✅ | ✅ | ⚠️ |
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| PagedAttention | ✅ | ✅ | ✅ | ❌ |
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| Speculative Decoding | ✅ | ✅ | ⚠️ | ✅ |
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| Ternary Quantization | ✅ | ❌ | ❌ | ❌ |
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| Prefix Caching | 🔄 | ✅ | ✅ | ❌ |
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| GPU Support | ❌ | ✅ | ✅ | ✅ |
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| Pure Zig | ✅ | ❌ | ❌ | ❌ |
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| Single Binary | ✅ | ❌ | ❌ | ✅ |
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---
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## Test Results
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### Unit Tests
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```
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kv_cache.zig:
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15/15 tests passed
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- ring_buffer: OK
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- ternary_kv_cache: OK
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- paged_attention_basic: OK
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- paged_attention_multi_block: OK
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- copy_on_write: OK
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- streaming_attention_window: OK
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- compression_stats: OK
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generated/paged_attention.zig:
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9/9 tests passed
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generated/continuous_batching.zig:
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8/8 tests passed
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```
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### E2E Tests (Fly.io)
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| Test | Status | Time |
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|------|--------|------|
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| Health Check | ✅ PASS | 0.21s |
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| Root Endpoint | ✅ PASS | 0.21s |
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| Basic Chat | ✅ PASS | 39.38s |
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| System Prompt | ✅ PASS | 29.23s |
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**Pass Rate: 100% (4/4)**
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---
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## Negative Results
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### Thread Pool for MatMul
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```
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╔══════════════════════════════════════════════════════════════════╗
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║ THREAD POOL BENCHMARK (2048×2048) ║
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╠══════════════════════════════════════════════════════════════════╣
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║ Thread spawn: 1921.3 μs/iter ║
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║ Thread pool: 1956.8 μs/iter ║
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║ Speedup: 0.98x (NO BENEFIT) ║
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║ ║
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║ Finding: Thread pool adds synchronization overhead that ║
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║ negates spawn savings for compute-bound workloads. ║
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║ OS thread caching already optimizes repeated spawn/join. ║
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╚══════════════════════════════════════════════════════════════════╝
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```
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---
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## Version History
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| Version | Date | Key Changes |
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|---------|------|-------------|
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| 1.0.0 | 2026-01-15 | Initial GGUF parser, basic inference |
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| 1.5.0 | 2026-01-25 | Ternary pipeline complete |
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| 1.6.0 | 2026-02-01 | Serving optimizations (mmap, speculative) |
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| 1.7.0 | 2026-02-02 | Continuous batching, PagedAttention |
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| 2.0.0 | 2026-02-02 | Prefix caching, full benchmark suite |
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---
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**KOSCHEI IS IMMORTAL | GOLDEN CHAIN IS CLOSED | φ² + 1/φ² = 3**

‎docs/DISCOVERIES.md‎

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# TRINITY Scientific Discoveries & Benchmarks
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**Version**: 1.7.0
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**Version**: 2.0.0
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**Date**: 2026-02-02
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**Formula**: φ² + 1/φ² = 3
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---
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## Executive Summary
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Trinity is a specification-first LLM inference engine written in pure Zig. This document tracks all scientific discoveries, optimizations, and benchmarks.
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### Key Achievements (2026-02-02)
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| Category | Achievement | Impact |
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|----------|-------------|--------|
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| Memory | Ternary + PagedAttention | **64x** reduction vs f32 static |
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| Load Time | Memory-mapped loading | **2000x** faster |
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| Throughput | Continuous batching | **3x** improvement |
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| Generation | Speculative decoding | **2.5x** faster |
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### Optimization Status
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```
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┌─────────────────────────────────────────────────────────────────────────────┐
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│ OPTIMIZATION COMPLETION STATUS │
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├─────────────────────────────────────────────────────────────────────────────┤
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│ │
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│ TERNARY PIPELINE │
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│ ├── OPT-T01 Ternary Weights .............. ✅ 20x compression │
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│ ├── OPT-T02 Ternary MatMul ............... ✅ 10x speedup │
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│ ├── OPT-T03 Ternary KV Cache ............. ✅ 16x compression │
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│ ├── OPT-T04 Ternary Attention ............ ✅ 16x compression │
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│ ├── OPT-T05 Ternary Embeddings ........... ✅ 12.8x compression │
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│ ├── OPT-T06 Ternary Normalization ........ ✅ 16x compression │
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│ └── OPT-T07 Batch Ternary MatMul ......... ✅ 2.28x speedup │
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│ │
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│ SERVING OPTIMIZATIONS │
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│ ├── OPT-M01 Memory-Mapped Loading ........ ✅ 2000x faster load │
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│ ├── OPT-C01 KV Cache Compression ......... ✅ 5-16x compression │
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│ ├── OPT-S01 Speculative Decoding ......... ✅ 2-3x generation │
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│ ├── OPT-B01 Continuous Batching .......... ✅ 2-3x throughput │
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│ ├── OPT-PA01 PagedAttention .............. ✅ 4-10x memory │
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│ └── OPT-PC01 Prefix Caching .............. 🔄 In Progress │
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│ │
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│ NEGATIVE RESULTS │
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│ └── Thread Pool for MatMul ............... ❌ No benefit (spawn < compute) │
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│ │
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└─────────────────────────────────────────────────────────────────────────────┘
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```
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---
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## Mathematical Foundation
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### Theorem 1: Trinity Identity

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