# SOLLOL Benchmarking Guide

## Current Validation Status

### ✅ What's Been Tested
- **Single Ollama Node Performance:** Baseline metrics established
  - 50 requests to llama3.2 model
  - 100% success rate
  - Average latency: 5,659ms
  - See: `benchmarks/results/ollama_benchmark_llama3.2_*.json`

### ⚠️ What Needs Validation
- **SOLLOL vs Round-Robin Comparison:** Not yet run
- **Multi-node intelligent routing:** Requires cluster setup
- **Performance improvement claims:** Based on architecture design, not measured

## How to Run Comparative Benchmarks

### Prerequisites
1. **Multiple Ollama nodes** (at least 3 for meaningful comparison)
2. **SOLLOL gateway** running and connected to nodes
3. **Same test workload** for fair comparison

### Step 1: Set Up Test Cluster

#### Option A: Docker Compose (Easiest)
```bash
# Start 3 Ollama nodes + SOLLOL gateway
docker-compose up -d

# Wait for all services to be healthy
docker-compose ps

# Verify nodes are discoverable
curl http://localhost:11434/api/health
```

#### Option B: Manual Setup
```bash
# Terminal 1: Start Ollama node 1
ollama serve --port 11434

# Terminal 2: Start Ollama node 2
ollama serve --port 11435

# Terminal 3: Start Ollama node 3
ollama serve --port 11436

# Terminal 4: Configure and start SOLLOL
cat > config/hosts.txt << EOF
http://localhost:11434
http://localhost:11435
http://localhost:11436
EOF

sollol up --port 8000
```

### Step 2: Run Comparative Benchmark

```bash
# This will test both round-robin and SOLLOL routing
python benchmarks/run_benchmarks.py \
  --sollol-url http://localhost:8000 \
  --hosts localhost:11434,localhost:11435,localhost:11436 \
  --duration 60 \
  --concurrency 10 \
  --output benchmarks/results/comparison_$(date +%Y%m%d_%H%M%S).json
```

### Step 3: Analyze Results

The benchmark will generate a comparison table:

```
Metric                   Round-Robin    SOLLOL         Improvement
─────────────────────────────────────────────────────────────────
Success Rate             95.2%          98.7%          +3.5%
Avg Latency              2,340ms        1,450ms        -38%
P95 Latency              5,120ms        2,560ms        -50%
Requests/sec             8.2            12.6           +52%
```

### Step 4: Commit Real Results

```bash
git add benchmarks/results/comparison_*.json
git commit -m "Add validated comparative benchmark results"
```

## What Performance Claims Need Validation

### From README - Currently Unvalidated

| Claim | Status | Evidence Needed |
|-------|--------|----------------|
| "38% faster responses" | ⚠️ Projected | Comparative benchmark showing latency reduction |
| "50% P95 latency reduction" | ⚠️ Projected | P95 metrics from SOLLOL vs baseline |
| "52% throughput improvement" | ⚠️ Projected | Requests/sec comparison |
| "4x speedup with parallel execution" | ⚠️ Theoretical | Multi-agent workload test |

### What Would Validate These Claims

**Minimum viable validation:**
1. 3 Ollama nodes running same model
2. 100 requests through naive round-robin load balancer
3. 100 requests through SOLLOL with intelligent routing
4. Compare metrics side-by-side
5. Document actual improvement percentages

**Full validation:**
1. Multiple test scenarios (simple, complex, mixed)
2. Different cluster sizes (3, 5, 10 nodes)
3. Various models (small, medium, large)
4. Workload patterns (burst, sustained, mixed priority)
5. Network conditions (LAN, WAN, high latency)

## Current Honest Assessment

**What SOLLOL definitely provides:**
- ✅ Working intelligent routing engine (code exists and is reviewable)
- ✅ Priority queue implementation (57 tests passing)
- ✅ Multi-node orchestration (architecture validated)
- ✅ Auto-discovery and failover (implementation complete)

**What's unproven without comparative benchmarks:**
- ⚠️ Actual latency improvements vs naive load balancing
- ⚠️ Real-world throughput gains
- ⚠️ Intelligent routing making better decisions than round-robin

## For Recruiters/Employers

**What this shows about engineering capability:**
1. **System Design:** Distributed systems architecture with intelligent routing
2. **Code Quality:** 75+ Python modules, typed, tested, documented
3. **Production Readiness:** K8s manifests, Docker Compose, CI/CD, monitoring
4. **Honest Engineering:** Clear about what's validated vs projected

**What's missing for "production proven":**
- Large-scale comparative benchmarks showing measurable improvement
- Real-world deployment validation with traffic

**Why this is still valuable:**
The implementation quality and architecture are solid. The benchmark gap doesn't invalidate the engineering - it just means the performance claims need empirical validation. The infrastructure is production-ready; the performance optimization claims need proof.

## Next Steps for Validation

1. **Priority 1:** Run docker-compose cluster and comparative benchmark
2. **Priority 2:** Document actual measured improvements (or lack thereof)
3. **Priority 3:** Update README with honest, measured claims
4. **Priority 4:** Add performance regression testing to CI/CD

## Running Your Own Tests

If you're evaluating SOLLOL:

```bash
# Clone and set up
git clone https://github.com/BenevolentJoker-JohnL/SOLLOL.git
cd SOLLOL

# Quick single-node baseline (no Docker needed)
python benchmarks/simple_ollama_benchmark.py llama3.2 50

# Full comparative test (requires Docker)
docker-compose up -d
python benchmarks/run_benchmarks.py --sollol-url http://localhost:8000 --duration 60

# Results will be in benchmarks/results/
```

**Expected time:** ~5 minutes for baseline, ~10 minutes for full comparison

## Contact

Found issues with benchmarks or want to contribute results from your hardware? Open an issue with your benchmark output.
