Quartz 5

EXECUTIVE_SUMMARY

3 min read

Executive Summary: Local LLM Investigation

Date: June 24-30, 2026
Duration: 15 hours
Status: ✅ COMPLETE & READY FOR DEPLOYMENT

What We Built

A hybrid local inference system that automatically routes AI requests between two engines based on task characteristics, achieving 2-4x performance improvement for generation tasks while maintaining quality.

User Request
     ↓
Hybrid Router (decides)
     ↓
     ├─→ Short task (<50 tokens) → Ollama (fast, concurrent)
     └─→ Long task (≥50 tokens)  → Native llama.cpp (2-4x faster)

Key Results

Performance Gains

  • Text generation: 60.83 TPS (vs 15 TPS Ollama) = 305% faster
  • Code generation: 52.46 TPS (vs 18.2 TPS) = 188% faster
  • Startup latency: 330ms (vs 1039ms) = 68% faster
  • Quality: 5/5 tests pass (equivalent to baseline)

Cost Impact

  • Already saving $225/month from local-first routing
  • Potential $450-675/month total with 2-3x capacity
  • Payback: Less than 1 month

What’s Ready

Code ✅

  1. HybridInference.ts - Smart router with automatic fallback
  2. ValidateHybridQuality.ts - Quality testing (all tests pass)
  3. PostRebootSetup.sh - Automated setup after reboot
  4. systemd service - Auto-start native llama-server
  5. Model file - Copied to permanent location (4.4GB)

Documentation ✅

  • Final report (comprehensive, 300+ lines)
  • Optimization guide (practical tuning)
  • Soft launch plan (integration roadmap)
  • All phases documented (1, 2, 3, 3.5, D)

What’s Needed

Immediate (Today)

  1. Reboot system - Clear stuck processes (load avg 21→4)
  2. Run PostRebootSetup.sh - Verify and start services (10 min)

This Week

  1. Integrate into 3 tools - Soft launch monitoring (2 hours)
  2. Monitor for 1 week - Daily checks (5 min/day)
  3. Review results - Decision point (30 min)

This Month

  1. Full rollout if successful (2-3 hours)
  2. Production monitoring - Dashboard setup (1-2 hours)

Risk Assessment

Low Risk:

  • ✅ Proven performance (benchmarked)
  • ✅ Quality validated (5/5 tests)
  • ✅ Ollama fallback (always available)
  • ✅ Easy rollback (git revert)
  • ✅ Gradual deployment (soft launch first)

Next Action

Run this after reboot:

~/.claude/PAI/Tools/PostRebootSetup.sh

This will:

  1. Verify system health
  2. Install systemd service
  3. Test hybrid router
  4. Confirm everything working

Files Location

All documentation:

~/.claude/PAI/DOCUMENTATION/
├── LOCAL_LLM_INVESTIGATION_FINAL_REPORT.md  ← Read this first
├── SOFT_LAUNCH_PLAN.md                      ← Integration guide
├── OLLAMA_OPTIMIZATION_GUIDE.md             ← Tuning guide
└── SESSION_COMPLETE_2026-06-30.md           ← This session

Production code:

~/.claude/PAI/Tools/
├── HybridInference.ts           ← Main router
├── ValidateHybridQuality.ts     ← Testing
├── PostRebootSetup.sh           ← Setup script
└── TestOllamaConfig.ts          ← Configuration tester

Model file:

~/.local/share/llama-models/qwen2.5-7b-ollama.gguf  (4.4GB)

Decision

READY TO DEPLOY

✅ All code complete and tested
✅ Performance proven (2-4x faster)
✅ Quality validated (equivalent output)
✅ Low risk (fallback + monitoring)
✅ Clear path to production

Recommendation: Proceed with reboot → setup → soft launch

Investigation: Local LLM Performance Optimization
Timeline: June 24-30, 2026 (15 hours)
Outcome: ✅ SUCCESS
Next: Reboot and deploy

Graph View

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