# SOLLOL Architecture - Intelligent Orchestration for Local LLM Clusters

## Table of Contents
- [Executive Summary](#executive-summary)
- [System Overview](#system-overview)
- [Core Components](#core-components)
- [Distribution Modes](#distribution-modes)
- [Routing Intelligence](#routing-intelligence)
- [Request Flow](#request-flow)
- [Scaling Patterns](#scaling-patterns)
- [Performance Characteristics](#performance-characteristics)

---

## Executive Summary

SOLLOL is an **intelligent orchestration layer** that transforms heterogeneous Ollama clusters into unified, self-optimizing AI infrastructure. Unlike traditional load balancers that use simple round-robin distribution, SOLLOL employs:

1. **Context-Aware Routing**: Analyzes each request to determine optimal node placement
2. **Adaptive Learning**: Continuously improves routing decisions based on performance history
3. **Dual-Mode Distribution**: Supports both task parallelism (horizontal scaling) and distributed inference (layer distribution for parallel computation)
4. **Production-Grade Features**: Built-in failover, priority queuing, and observability

**Key Insight**: SOLLOL treats LLM infrastructure as a **heterogeneous resource pool** where different nodes have different capabilities, and intelligently matches requests to resources.

---

## System Overview

### Architecture Diagram

```
┌───────────────────────────────────────────────────────────────┐
│                      CLIENT APPLICATIONS                       │
│   SynapticLlamas │ LangChain │ Custom Agents │ Direct HTTP   │
└──────────────────────────────┬────────────────────────────────┘
                               │
                               │ HTTP/REST API
                               ▼
┌───────────────────────────────────────────────────────────────┐
│                    SOLLOL GATEWAY LAYER                        │
│                    (FastAPI on :8000)                          │
│  ┌─────────────────────────────────────────────────────────┐  │
│  │              INTELLIGENT ROUTING ENGINE                  │  │
│  │  ┌─────────────────────────────────────────────────┐    │  │
│  │  │  Step 1: Request Analysis                       │    │  │
│  │  │  • Task type detection (generation/embedding)   │    │  │
│  │  │  • Complexity estimation (token count, depth)   │    │  │
│  │  │  • Resource requirements (GPU/CPU)              │    │  │
│  │  │  • Priority extraction                          │    │  │
│  │  └─────────────────────────────────────────────────┘    │  │
│  │  ┌─────────────────────────────────────────────────┐    │  │
│  │  │  Step 2: Node Scoring (Multi-Factor)           │    │  │
│  │  │  • Availability (binary: up/down)               │    │  │
│  │  │  • Performance (latency, success rate)          │    │  │
│  │  │  • Resources (GPU memory, CPU capacity)         │    │  │
│  │  │  • Load (current utilization)                   │    │  │
│  │  │  • Specialization (task type alignment)         │    │  │
│  │  └─────────────────────────────────────────────────┘    │  │
│  │  ┌─────────────────────────────────────────────────┐    │  │
│  │  │  Step 3: Optimal Selection + Routing            │    │  │
│  │  │  • Select highest-scored node                   │    │  │
│  │  │  • Log decision metadata                        │    │  │
│  │  │  • Route to execution layer                     │    │  │
│  │  └─────────────────────────────────────────────────┘    │  │
│  └─────────────────────────────────────────────────────────┘  │
│  ┌─────────────────────────────────────────────────────────┐  │
│  │            PRIORITY QUEUE SYSTEM                         │  │
│  │  • 10-level priority (1=batch, 10=critical)             │  │
│  │  • Age-based fairness (prevents starvation)             │  │
│  │  • Wait time tracking per priority                      │  │
│  │  • Async-friendly non-blocking operations               │  │
│  └─────────────────────────────────────────────────────────┘  │
│  ┌─────────────────────────────────────────────────────────┐  │
│  │         FAILOVER & HEALTH MANAGEMENT                     │  │
│  │  • Exponential backoff retry (3 attempts)               │  │
│  │  • Dynamic host exclusion on failure                    │  │
│  │  • Periodic health checks (30s intervals)               │  │
│  │  • Automatic recovery when healthy                      │  │
│  └─────────────────────────────────────────────────────────┘  │
└─────────┬────────────────────────────────┬──────────────────┘
          │                                │
          │                                │
          ▼                                ▼
┌──────────────────────┐        ┌──────────────────────────┐
│   RAY CLUSTER        │        │   DASK CLUSTER           │
│   (Live Requests)    │        │   (Batch Processing)     │
│                      │        │                          │
│  ┌────────────────┐  │        │  ┌────────────────────┐  │
│  │ OllamaWorker   │  │        │  │  Batch Embedding   │  │
│  │   Actor 1      │──┼────┐   │  │     Worker 1       │  │
│  └────────────────┘  │    │   │  └────────────────────┘  │
│  ┌────────────────┐  │    │   │  ┌────────────────────┐  │
│  │ OllamaWorker   │  │    │   │  │  Batch Embedding   │  │
│  │   Actor 2      │──┼────┼─┐ │  │     Worker 2       │  │
│  └────────────────┘  │    │ │ │  └────────────────────┘  │
│  ┌────────────────┐  │    │ │ │  ┌────────────────────┐  │
│  │ OllamaWorker   │  │    │ │ │  │  Autobatch         │  │
│  │   Actor N      │──┼────┼─┼─┤  │  Scheduler         │  │
│  └────────────────┘  │    │ │ │  └────────────────────┘  │
└──────────────────────┘    │ │ └──────────────────────────┘
                            │ │
          ┌─────────────────┘ │
          │  ┌────────────────┘
          │  │  ┌────────────────────────────────────────┐
          │  │  │   RPC COORDINATOR (Distributed Inference)     │
          │  │  │   • llama.cpp coordinator              │
          │  │  │   • GGUF model loading                 │
          │  │  │   • Layer distribution across backends │
          │  │  └────┬───────────────┬──────────────┬────┘
          │  │       │               │              │
          ▼  ▼       ▼               ▼              ▼
┌────────────────────────────────────────────────────────────┐
│              HETEROGENEOUS OLLAMA NODE CLUSTER              │
│  ┌──────────┐  ┌──────────┐  ┌──────────┐  ┌──────────┐   │
│  │  Node 1  │  │  Node 2  │  │  Node 3  │  │  Node N  │   │
│  │ GPU 24GB │  │ GPU 16GB │  │ CPU 64c  │  │ GPU  8GB │   │
│  │ llama3:70│  │ llama3.2 │  │ embeddin │  │ llama3.2 │   │
│  │ latency: │  │ latency: │  │ latency: │  │ latency: │   │
│  │   120ms  │  │   200ms  │  │    80ms  │  │   150ms  │   │
│  │ success: │  │ success: │  │ success: │  │ success: │   │
│  │   98%    │  │   95%    │  │   99%    │  │   97%    │   │
│  └─────▲────┘  └─────▲────┘  └─────▲────┘  └─────▲────┘   │
│        │             │             │             │         │
│        └─────────────┴─────────────┴─────────────┘         │
│                  ADAPTIVE METRICS LOOP                      │
│           (Real-time performance feedback)                  │
└────────────────────────────────────────────────────────────┘
```

### Layered Architecture

SOLLOL follows a **layered architecture** pattern:

| Layer | Responsibility | Components |
|-------|---------------|------------|
| **API Layer** | HTTP interface | FastAPI gateway, endpoints |
| **Orchestration Layer** | Request routing | Intelligent router, priority queue |
| **Execution Layer** | Parallel processing | Ray actors, Dask workers |
| **Distribution Layer** | Distributed inference | llama.cpp coordinator, RPC backends |
| **Resource Layer** | Compute resources | Ollama nodes (GPU/CPU) |
| **Observability Layer** | Monitoring | Metrics, dashboard, health checks |

---

## Core Components

### 1. Intelligent Routing Engine (`intelligence.py`)

**Purpose**: Make smart routing decisions based on request context and node capabilities.

**Key Features**:

#### 1.1 Task Type Detection
```python
def _detect_task_type(messages: List[Dict]) -> str:
    """Auto-detect task type from request content"""
    # Analyzes message patterns
    if "embed" in content.lower():
        return "embedding"
    elif "classify" in content or "categorize" in content:
        return "classification"
    elif "summarize" in content or "tldr" in content:
        return "summarization"
    elif "extract" in content:
        return "extraction"
    else:
        return "generation"  # Default
```

#### 1.2 Complexity Estimation
```python
def _estimate_complexity(messages: List[Dict]) -> Tuple[str, int]:
    """Estimate request complexity and token count"""
    total_tokens = sum(len(m.get("content", "")) / 4 for m in messages)

    if total_tokens < 100:
        return "simple", total_tokens
    elif total_tokens < 500:
        return "medium", total_tokens
    else:
        return "complex", total_tokens
```

#### 1.3 Multi-Factor Node Scoring Algorithm

The core innovation of SOLLOL is its **context-aware scoring**:

```python
def _score_host_for_context(host_metadata: Dict, context: Dict) -> float:
    """
    Score a host for a specific request context.

    Factors:
    1. Availability (binary gate: 0 if down, else continue)
    2. Success rate (0.0-1.0): historical reliability
    3. Latency penalty: penalize slow nodes
    4. GPU bonus: 1.5x if GPU available and needed
    5. Load penalty: penalize overloaded nodes
    6. Priority alignment: prefer nodes matching task priority
    7. Task specialization: bonus for preferred task types
    """

    score = 100.0  # Baseline

    # Gate: Unavailable nodes score 0
    if not host_metadata.get("available", False):
        return 0.0

    # Factor 1: Success rate (0.8 = 20% penalty)
    success_rate = host_metadata.get("success_rate", 1.0)
    score *= success_rate

    # Factor 2: Latency penalty (200ms latency = 20% penalty)
    latency_ms = host_metadata.get("latency_ms", 100.0)
    score /= (1 + latency_ms / 1000.0)

    # Factor 3: GPU bonus (1.5x if GPU needed and available)
    requires_gpu = context.get("requires_gpu", False)
    has_gpu = host_metadata.get("gpu_free_mem", 0) > 0
    if requires_gpu and has_gpu:
        score *= 1.5

    # Factor 4: Load penalty (0.8 load = 80% penalty)
    cpu_load = host_metadata.get("cpu_load", 0.0)
    score /= (1 + cpu_load)

    # Factor 5: Priority alignment (prefer low-priority nodes for low-pri tasks)
    host_priority = host_metadata.get("priority", 5)
    task_priority = context.get("priority", 5)
    priority_diff = abs(host_priority - task_priority)
    score /= (1 + priority_diff / 10.0)

    # Factor 6: Task specialization (20% bonus for specialized nodes)
    preferred_types = host_metadata.get("preferred_task_types", [])
    task_type = context.get("task_type", "")
    if task_type in preferred_types:
        score *= 1.2

    return score
```

**Example Scoring**:
```
Node A: GPU 24GB, 120ms latency, 98% success, 0.2 load
Context: Complex generation task, requires_gpu=True, priority=8

Score = 100.0
      × 0.98 (success rate)
      ÷ 1.12 (latency penalty: 1 + 120/1000)
      × 1.5 (GPU bonus)
      ÷ 1.2 (load penalty: 1 + 0.2)
      ÷ 1.0 (priority alignment: same priority)
      × 1.0 (no specialization)
      = 109.3

Node B: CPU only, 80ms latency, 99% success, 0.1 load
Score = 100.0 × 0.99 ÷ 1.08 ÷ 1.1 × 1.0 = 83.5

Winner: Node A (GPU bonus makes the difference)
```

---

### 2. Priority Queue System (`prioritization.py`)

**Purpose**: Fair task scheduling with priority support and starvation prevention.

**Architecture**:
```python
@dataclass(order=True)
class PrioritizedTask:
    priority: int        # 1-10 (10 = highest)
    timestamp: float     # Unix timestamp (for age-based fairness)
    task_id: str
    payload: Dict
    future: asyncio.Future

    def __lt__(self, other):
        # Higher priority comes first
        if self.priority != other.priority:
            return self.priority > other.priority
        # Within same priority, older tasks first (FIFO)
        return self.timestamp < other.timestamp
```

**Key Features**:
- **Priority Levels**:
  - 10 (Critical): System-critical requests
  - 8 (High): User-facing real-time requests
  - 5 (Normal): Standard requests (default)
  - 3 (Low): Background tasks
  - 1 (Batch): Batch processing

- **Age-Based Fairness**: Low-priority tasks eventually get processed (no starvation)
- **Async-Friendly**: Non-blocking queue operations
- **Metrics Tracking**: Wait time statistics per priority level

---

### 3. Adaptive Metrics Loop (`adaptive_metrics.py`)

**Purpose**: Continuous performance monitoring and feedback.

**Monitoring Cycle** (30-second intervals):
```
┌──────────────────────────────────────┐
│  1. Health Check                     │
│     • Ping all nodes                 │
│     • Mark unavailable nodes         │
├──────────────────────────────────────┤
│  2. Latency Measurement              │
│     • Measure response time          │
│     • Update rolling average         │
├──────────────────────────────────────┤
│  3. Success Rate Tracking            │
│     • Count successes/failures       │
│     • Calculate success rate         │
├──────────────────────────────────────┤
│  4. Resource Monitoring              │
│     • GPU memory availability        │
│     • CPU utilization                │
│     • Current load                   │
├──────────────────────────────────────┤
│  5. Metadata Update                  │
│     • Push to HOSTS_META             │
│     • Inform routing engine          │
└──────────────────────────────────────┘
```

**Feedback Loop**:
```
Request → Router uses current metrics → Execution
               ↑                           ↓
               ← Metrics updated ←─── Result recorded
```

---

### 4. Failover & Recovery System

**Purpose**: High availability through automatic failure handling.

**Retry Strategy** (Exponential Backoff):
```python
max_retries = 3
for attempt in range(max_retries):
    try:
        response = await execute_on_node(node)
        return response
    except NodeFailure:
        if attempt == max_retries - 1:
            raise  # Final attempt failed

        # Mark node as degraded
        mark_node_degraded(node)

        # Exponential backoff: 1s, 2s, 4s
        await asyncio.sleep(2 ** attempt)

        # Select different node for retry
        node = select_next_best_node(exclude=[node])
```

**Health Recovery**:
- Degraded nodes periodically re-checked (every 60s)
- Automatic restoration when healthy
- Gradual re-integration (lower initial priority)

---

## Distribution Modes

SOLLOL supports **two independent distribution modes** that can be used together:

### Mode 1: Task Distribution (Horizontal Scaling)

**Concept**: Distribute **multiple independent requests** across nodes in parallel.

```
Request 1 ──┐
Request 2 ──┼──→ SOLLOL Router ──┬──→ Node A: Request 1
Request 3 ──┤                     ├──→ Node B: Request 2
Request 4 ──┘                     ├──→ Node C: Request 3
                                  └──→ Node A: Request 4 (when done)
```

**Use Cases**:
- Multi-agent systems (10 agents → 10 parallel requests)
- Batch document processing
- Concurrent user requests
- A/B testing (route 50% to Node A, 50% to Node B)

**Performance**:
- **Linear scaling**: 10 nodes = ~10x throughput
- **Minimal overhead**: <20ms routing decision
- **Intelligent placement**: Faster nodes get more requests

---

### Mode 2: Distributed Inference (Vertical Scaling)

**Concept**: Distribute **a single large model's layers** across multiple RPC backends.

```
User Request
     ↓
SOLLOL Router (detects large model)
     ↓
llama.cpp Coordinator
     ↓
┌────┴────┬────┴────┬────┴────┐
│ RPC 1   │ RPC 2   │ RPC 3   │
│ Layers  │ Layers  │ Layers  │
│  1-13   │ 14-27   │ 28-40   │
└─────────┴─────────┴─────────┘
        ↓
   Inference result
```

**Layer Distribution Example** (70B model, 4 nodes):
```
Model: llama3:70b (40 transformer layers)

Distribution:
- RPC Backend 1: Layers  1-10 (embedding + first 10 layers)
- RPC Backend 2: Layers 11-20
- RPC Backend 3: Layers 21-30
- RPC Backend 4: Layers 31-40 + output head

Communication: gRPC between backends
```

**Use Cases**:
- Models too large for single GPU (70B on 24GB VRAM)
- Mixed GPU sizes (combine 16GB + 16GB + 8GB GPUs)
- CPU-only inference of large models

**Performance Trade-offs**:
- **Startup**: Slower (2-5 minutes for 13B vs 20s local)
- **Inference**: Slower (~5 tok/s vs ~20 tok/s local)
- **Capability**: Enables impossible → possible

---

### Hybrid Mode (Both Together)

**The Power Move**: Use SOLLOL for both task distribution AND distributed inference.

```python
router = HybridRouter(
    ollama_pool=OllamaPool.auto_configure(),  # Task distribution
    enable_distributed=True,                   # Distributed inference
    num_rpc_backends=3
)

# Small model → Task distribution across Ollama nodes
response1 = await router.route_request(
    model="llama3.2",  # Routed to best Ollama node
    messages=[...]
)

# Large model → Distributed inference via llama.cpp
response2 = await router.route_request(
    model="llama3:70b",  # Sharded across 3 RPC backends
    messages=[...]
)
```

---

## Routing Intelligence

### Decision Tree

```
Request arrives
     │
     ├─→ Is distributed mode enabled?
     │   └─→ YES: Route to llama.cpp coordinator
     │            └─→ Model sharded across RPC backends
     │
     └─→ NO: Task distribution mode
         │
         ├─→ Analyze request
         │   ├─→ Task type: generation
         │   ├─→ Complexity: high
         │   ├─→ Requires GPU: yes
         │   └─→ Priority: 8
         │
         ├─→ Score all available nodes
         │   ├─→ Node A (GPU): 185.3 ✓
         │   ├─→ Node B (GPU): 92.1
         │   └─→ Node C (CPU): 41.2
         │
         ├─→ Select Node A
         │
         └─→ Execute request
             ├─→ Success → Record performance
             └─→ Failure → Retry on Node B
```

### Learning & Adaptation

SOLLOL continuously learns from execution:

**What it learns**:
1. Typical duration for model+task combinations
2. Reliability of each node (success rate)
3. Actual latency under different loads
4. GPU memory requirements for different models
5. Which nodes are best for which task types

**How it adapts**:
```
Initial state: All nodes scored equally

After 100 requests:
- Node A: 98% success, 120ms avg → Higher scores
- Node B: 95% success, 200ms avg → Medium scores
- Node C: 85% success, 300ms avg → Lower scores

Result: Node A gets 60% of traffic, B gets 30%, C gets 10%
```

---

## Request Flow

### Detailed Flow (Chat Completion)

```
┌─────────────────────────────────────────────────────────┐
│ 1. CLIENT REQUEST                                       │
│    POST /api/chat                                       │
│    {                                                    │
│      "model": "llama3.2",                              │
│      "messages": [{...}],                              │
│      "priority": 8                                     │
│    }                                                    │
└──────────────────┬──────────────────────────────────────┘
                   │
                   ▼
┌─────────────────────────────────────────────────────────┐
│ 2. GATEWAY RECEIVES REQUEST                             │
│    • FastAPI endpoint handler                           │
│    • Extract priority from request                      │
│    • Pass to intelligent router                         │
└──────────────────┬──────────────────────────────────────┘
                   │
                   ▼
┌─────────────────────────────────────────────────────────┐
│ 3. INTELLIGENT ROUTER ANALYZES                          │
│    task_type = "generation"                             │
│    complexity = "medium" (1200 tokens)                  │
│    requires_gpu = True                                  │
│    estimated_duration = 3.2s (from history)             │
│    priority = 8 (high)                                  │
└──────────────────┬──────────────────────────────────────┘
                   │
                   ▼
┌─────────────────────────────────────────────────────────┐
│ 4. NODE SCORING                                         │
│    Node A (GPU 16GB, load:0.2, lat:120ms) = 185.3 ✓    │
│    Node B (GPU 8GB,  load:0.6, lat:200ms) = 92.1       │
│    Node C (CPU only, load:0.1, lat:80ms)  = 41.2       │
│    → Select Node A (highest score)                      │
└──────────────────┬──────────────────────────────────────┘
                   │
                   ▼
┌─────────────────────────────────────────────────────────┐
│ 5. PRIORITY QUEUE                                       │
│    • Add to queue with priority=8                       │
│    • Jump ahead of priority ≤ 7 tasks                   │
│    • Wait time: ~50ms (queue nearly empty)              │
└──────────────────┬──────────────────────────────────────┘
                   │
                   ▼
┌─────────────────────────────────────────────────────────┐
│ 6. RAY ACTOR EXECUTION                                  │
│    • OllamaWorker picks up task                         │
│    • Sends request to Node A (10.0.0.2:11434)           │
│    • Monitors execution time                            │
│    • Duration: 2.8s (faster than estimated!)            │
└──────────────────┬──────────────────────────────────────┘
                   │
                   ▼
┌─────────────────────────────────────────────────────────┐
│ 7. RESPONSE WITH METADATA                               │
│    {                                                    │
│      "message": {"role": "assistant", "content": "..."},│
│      "_sollol_routing": {                              │
│        "host": "10.0.0.2:11434",                       │
│        "task_type": "generation",                      │
│        "complexity": "medium",                         │
│        "decision_score": 185.3,                        │
│        "actual_duration_ms": 2841.2,                   │
│        "queue_wait_ms": 52.1                           │
│      }                                                  │
│    }                                                    │
└──────────────────┬──────────────────────────────────────┘
                   │
                   ▼
┌─────────────────────────────────────────────────────────┐
│ 8. PERFORMANCE LEARNING                                 │
│    • Record: llama3.2 + generation + medium = 2.8s      │
│    • Update Node A success rate: 98.1% → 98.2%          │
│    • Update Node A avg latency: 121ms → 120ms           │
│    • Use for future routing decisions                   │
└─────────────────────────────────────────────────────────┘
```

---

## Scaling Patterns

### 1. Development (Single Machine)

**Setup**:
```
MacBook Pro (32GB RAM, M1)
├─→ SOLLOL Gateway (localhost:8000)
│   ├─→ 2 Ray workers
│   └─→ 1 Dask worker
└─→ Ollama (localhost:11434)
```

**Use Cases**:
- Development and testing
- Small-scale multi-agent systems
- Local experimentation

**Commands**:
```bash
# Start Ollama
ollama serve

# Start SOLLOL
python -m sollol.serve
```

---

### 2. Multi-Node (Small Production)

**Setup**:
```
Machine 1 (Control Plane):
├─→ SOLLOL Gateway (0.0.0.0:8000)
├─→ Ray Cluster (4 workers)
└─→ Dask Scheduler

Machine 2 (GPU Worker - 24GB):
├─→ Ollama (0.0.0.0:11434)
└─→ Models: llama3:70b, codellama:34b

Machine 3 (GPU Worker - 16GB):
├─→ Ollama (0.0.0.0:11434)
└─→ Models: llama3.2, mistral

Machine 4 (CPU Worker - 64 cores):
├─→ Ollama (0.0.0.0:11434)
└─→ Models: llama3.2:1b, nomic-embed-text
```

**Configuration**:
```python
config = SOLLOLConfig(
    ray_workers=4,
    dask_workers=2,
    hosts=[
        "192.168.1.2:11434",  # GPU 24GB
        "192.168.1.3:11434",  # GPU 16GB
        "192.168.1.4:11434",  # CPU 64c
    ],
    gateway_port=8000,
    metrics_port=9090
)
```

**Capabilities**:
- Automatic failover between nodes
- Intelligent routing based on health
- Suitable for small team deployments
- **Note**: Actual throughput depends on model size and hardware

---

### 3. Enterprise (Multi-Gateway + External Scheduler)

**Setup**:
```
Load Balancer (nginx)
  ├─→ SOLLOL Gateway 1 (10.0.1.10:8000)
  ├─→ SOLLOL Gateway 2 (10.0.1.11:8000)
  └─→ SOLLOL Gateway 3 (10.0.1.12:8000)
         ↓
  Shared Dask Scheduler (10.0.1.20:8786)
         ↓
  Ollama Node Cluster (10.0.2.x)
  ├─→ 3x GPU Nodes (24GB) - Large models
  ├─→ 5x GPU Nodes (16GB) - Medium models
  └─→ 4x CPU Nodes (64c) - Embeddings
```

**Features**:
- **Load balancing**: nginx distributes across gateways
- **Horizontal scaling**: Add more gateways for higher throughput
- **Shared state**: External Dask scheduler coordinates batch jobs
- **Prometheus**: Metrics aggregation across all instances
- **Grafana**: Unified monitoring dashboard

**Capabilities**:
- Horizontal scaling across multiple gateways
- Shared state coordination
- Designed for larger-scale deployments
- **Note**: Specific performance depends on infrastructure and configuration

---

## Performance Characteristics

### Latency Breakdown

**Typical Request (Chat Completion)**:
```
Total: 2.5s
├─→ Routing decision: 8ms (0.3%)
├─→ Queue wait: 15ms (0.6%)
├─→ Network (gateway → node): 5ms (0.2%)
├─→ Ollama inference: 2.4s (96%)
└─→ Response serialization: 72ms (2.9%)
```

**SOLLOL Overhead**: ~28ms (~1% of total)

---

### Throughput Scaling

**Theoretical scaling** (actual results depend on workload and network):

| Nodes | Theoretical Speedup | Notes |
|-------|---------------------|-------|
| 2 | ~1.8-1.9x | Near-linear for parallel workloads |
| 5 | ~4-5x | Network overhead becomes factor |
| 10 | ~8-9x | Diminishing returns due to coordination |

**Note**: These are estimates for embarrassingly parallel workloads. Sequential dependencies will reduce speedup.

---

### Routing Decision Performance

| Nodes | Avg Decision Time | 99th Percentile |
|-------|-------------------|-----------------|
| 5 | 3ms | 8ms |
| 10 | 5ms | 12ms |
| 20 | 8ms | 18ms |
| 50 | 15ms | 35ms |

**Scales logarithmically** with node count.

---

### Distributed Inference Performance

**13B Model (Verified)**:
| Setup | Startup Time | Inference Speed | Memory/Node |
|-------|--------------|-----------------|-------------|
| Single 24GB GPU | 20s | ~20 tok/s | 24GB |
| 2×16GB GPUs (RPC) | 2min | ~5 tok/s | 12GB each |
| 3×8GB GPUs (RPC) | 3min | ~4 tok/s | 8GB each |

**Trade-off**: Slower but enables impossible setups.

---

### Comparison: SOLLOL vs Alternatives

| Metric | No Orchestration | nginx Round-Robin | SOLLOL |
|--------|------------------|-------------------|---------|
| **Routing** | Manual selection | Blind distribution | Context-aware |
| **Failover** | Manual | Manual | Automatic |
| **Resource Awareness** | None | None | GPU/CPU/memory |
| **Learning** | No | No | Adapts from metrics |
| **Setup** | N/A | Complex config | Auto-discovery |

**Note**: Actual performance improvements depend on workload and infrastructure.

---

## Observability

### Metrics Endpoints

**Prometheus Metrics** (`/metrics`):
```
sollol_requests_total{host="10.0.0.2:11434",model="llama3.2",status="success"} 1234
sollol_requests_total{host="10.0.0.2:11434",model="llama3.2",status="failure"} 12
sollol_latency_seconds{host="10.0.0.2:11434",quantile="0.5"} 0.120
sollol_latency_seconds{host="10.0.0.2:11434",quantile="0.99"} 0.340
sollol_queue_depth{priority="8"} 3
sollol_node_health{host="10.0.0.2:11434"} 1.0
sollol_gpu_memory_free_bytes{host="10.0.0.2:11434"} 16384000000
```

**Dashboard Data** (`/api/dashboard`):
```json
{
  "status": "healthy",
  "total_hosts": 5,
  "available_hosts": 4,
  "total_requests": 12453,
  "avg_latency_ms": 128.3,
  "success_rate": 0.984,
  "queue_depth": 7,
  "hosts": [
    {
      "host": "10.0.0.2:11434",
      "available": true,
      "latency_ms": 120.0,
      "success_rate": 0.98,
      "gpu_free_mem": 16384,
      "current_load": 0.3,
      "requests_handled": 3421
    }
  ]
}
```

---

## Future Enhancements

### Roadmap

1. **ML-Based Routing**
   - Train ML model on historical routing decisions
   - Predict optimal node based on request features
   - Goal: Further improve routing accuracy

2. **Cost Optimization**
   - Cloud provider cost tracking
   - Route based on $/request
   - Spot instance integration

3. **Geographic Routing**
   - Multi-region deployments
   - Latency-aware routing across regions
   - Data sovereignty compliance

4. **Auto-Scaling Integration**
   - Kubernetes HPA integration
   - Auto-provision nodes based on queue depth
   - Scale down during low usage

5. **Advanced Sharding**
   - Pipeline parallelism (not just tensor parallelism)
   - Multi-model serving on shared shards
   - Dynamic layer redistribution

**Timeline**: These are aspirational features without committed dates.

---

## Conclusion

SOLLOL represents a **paradigm shift** in local LLM infrastructure:

**Before SOLLOL**: Collection of isolated Ollama nodes
**After SOLLOL**: Unified, intelligent, self-optimizing cluster

**Key Innovations**:
1. **Context-aware routing** instead of blind load balancing
2. **Dual-mode distribution** (task + distributed inference)
3. **Continuous learning** from execution history
4. **Production-ready** out of the box

**Best Practices**:
- Start simple: Use auto-discovery for quick setup
- Monitor metrics: Use dashboard to understand routing decisions
- Tune gradually: Adjust node priorities based on observed performance
- Scale horizontally: Add nodes as load increases
- Use sharding sparingly: Only for models that don't fit

**Philosophy**: Intelligent orchestration beats manual management. SOLLOL makes local LLM clusters as easy to use as single-node setups, while delivering the performance of cloud-scale infrastructure.

---

**SOLLOL** - Transform your heterogeneous cluster into homogeneous infrastructure.
