sollol 0.3.1

Super Ollama Load Balancer with Intelligent Routing and Distributed Inference

SOLLOL - Super Ollama Load Balancer

Intelligent Load Balancing and Distributed Inference for Ollama

SOLLOL is a high-performance load balancer and distributed inference engine for Ollama, with support for llama.cpp RPC backends for models that don't fit on a single GPU.

Features

🚀 Core Features

• Intelligent Load Balancing: Adaptive routing based on node performance, GPU availability, and task complexity
• Auto-Discovery: Automatic detection of Ollama nodes and RPC backends on your network
• Connection Pooling: Efficient connection management with health monitoring
• Request Hedging: Duplicate requests to multiple nodes for lower latency
• Task Prioritization: Priority-based request queuing

🔗 Distributed Inference

• Hybrid Routing: Automatically routes small models to Ollama, large models to llama.cpp
• RPC Backend Support: Connect to llama.cpp RPC servers for distributed inference
• GGUF Auto-Resolution: Automatically extracts GGUFs from Ollama blob storage
• Zero Configuration: Auto-discovers RPC backends on your network

📊 Monitoring & Observability

• Real-time Metrics: Track performance, latency, and node health
• Web Dashboard: Monitor routing decisions and backend status
• Performance Learning: Adapts routing based on historical performance

Installation

From PyPI (when published)

pip install sollol

From Source

git clone https://github.com/BenevolentJoker-JohnL/SynapticLlamas.git
cd SynapticLlamas/sollol
pip install -e .

Quick Start

Basic Usage

from sollol import OllamaPool

# Auto-discover Ollama nodes and create pool
pool = OllamaPool.auto_configure()

# Make a chat request
response = pool.chat(
    model="llama3.2",
    messages=[{"role": "user", "content": "Hello!"}]
)
print(response)

With Distributed Inference

from sollol import HybridRouter, OllamaPool
from sollol.rpc_discovery import auto_discover_rpc_backends

# Discover RPC backends
rpc_backends = auto_discover_rpc_backends()

# Create hybrid router
router = HybridRouter(
    ollama_pool=OllamaPool.auto_configure(),
    rpc_backends=rpc_backends,
    enable_distributed=True
)

# Routes automatically: small models → Ollama, large models → llama.cpp
response = await router.route_request(
    model="llama3.1:405b",  # Automatically uses distributed inference
    messages=[{"role": "user", "content": "Explain quantum computing"}]
)

Auto-Discovery

from sollol.discovery import discover_ollama_nodes
from sollol.rpc_discovery import auto_discover_rpc_backends

# Discover Ollama nodes
ollama_nodes = discover_ollama_nodes()
print(f"Found {len(ollama_nodes)} Ollama nodes")

# Discover RPC backends for distributed inference
rpc_backends = auto_discover_rpc_backends()
print(f"Found {len(rpc_backends)} RPC backends")

Configuration

OllamaPool Options

from sollol import OllamaPool

pool = OllamaPool(
    nodes=[
        {"host": "10.9.66.154", "port": "11434"},
        {"host": "10.9.66.157", "port": "11434"}
    ],
    enable_intelligent_routing=True,  # Use smart routing
    exclude_localhost=False  # Include localhost in discovery
)

HybridRouter Options

from sollol import HybridRouter

router = HybridRouter(
    ollama_pool=pool,
    rpc_backends=[
        {"host": "192.168.1.10", "port": 50052},
        {"host": "192.168.1.11", "port": 50052}
    ],
    coordinator_host="127.0.0.1",
    coordinator_port=8080,
    enable_distributed=True,
    auto_discover_rpc=True  # Auto-discover RPC backends
)

Distributed Inference Setup

Option 1: Zero-Config Auto-Setup (Easiest!)

SOLLOL can automatically setup llama.cpp RPC backends for you:

from sollol import HybridRouter, OllamaPool

# Everything auto-configures AND auto-setups!
router = HybridRouter(
    ollama_pool=OllamaPool.auto_configure(),
    enable_distributed=True,
    auto_discover_rpc=True,  # Discover existing RPC servers
    auto_setup_rpc=True,     # Auto-build & start RPC servers if none found
    num_rpc_backends=2       # Number of backends to start
)

# SOLLOL will automatically:
# 1. Check for running RPC servers
# 2. If none found, clone llama.cpp
# 3. Build with RPC support
# 4. Start RPC servers
# 5. Configure hybrid routing

# Use it immediately!
response = await router.route_request(
    model="llama3.1:405b",
    messages=[{"role": "user", "content": "Hello!"}]
)

Or use the standalone auto-setup:

from sollol import auto_setup_rpc_backends

# Automatically setup RPC backends
backends = auto_setup_rpc_backends(
    num_backends=2,      # Start 2 RPC servers
    auto_build=True      # Build llama.cpp if needed
)
print(f"RPC backends ready: {backends}")
# Output: [{'host': '127.0.0.1', 'port': 50052}, {'host': '127.0.0.1', 'port': 50053}]

Option 2: Manual Setup (Full Control)

1. Start RPC Servers (Worker Nodes)

On each worker node:

# Build llama.cpp with RPC support
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
cmake -B build -DGGML_RPC=ON -DLLAMA_CURL=OFF
cmake --build build --config Release -j$(nproc)

# Start RPC server
./build/bin/rpc-server --host 0.0.0.0 --port 50052

2. Use SOLLOL with Auto-Discovery

from sollol import HybridRouter, OllamaPool

# Everything auto-configures!
router = HybridRouter(
    ollama_pool=OllamaPool.auto_configure(),
    enable_distributed=True,
    auto_discover_rpc=True  # Finds RPC servers automatically
)

# Use it
response = await router.route_request(
    model="llama3.1:405b",
    messages=[{"role": "user", "content": "Hello!"}]
)

API Reference

OllamaPool

Methods:

• chat(model, messages, priority=5, **kwargs) - Chat completion
• generate(model, prompt, priority=5, **kwargs) - Text generation
• embed(model, input, priority=5, **kwargs) - Generate embeddings
• get_stats() - Get pool statistics
• add_node(host, port) - Add a node to the pool
• remove_node(host, port) - Remove a node

HybridRouter

Methods:

• route_request(model, messages, **kwargs) - Route request to appropriate backend
• should_use_distributed(model) - Check if model should use distributed inference
• get_stats() - Get routing statistics

Discovery & Auto-Setup

Functions:

• discover_ollama_nodes(timeout=0.5) - Discover Ollama nodes on the network
• auto_discover_rpc_backends(port=50052) - Discover existing llama.cpp RPC backends
• auto_setup_rpc_backends(num_backends=1, auto_build=True) - Auto-setup RPC backends (clone, build, start)
• check_rpc_server(host, port, timeout=1.0) - Check if RPC server is running

Environment Variables

• OLLAMA_HOST - Default Ollama host (e.g., http://localhost:11434)
• LLAMA_RPC_BACKENDS - Comma-separated RPC backends (e.g., 192.168.1.10:50052,192.168.1.11:50052)

Performance

SOLLOL provides intelligent routing that adapts to:

• Node Performance: Routes requests to faster nodes
• GPU Availability: Prefers nodes with available GPU memory
• Task Complexity: Routes complex tasks to more capable nodes
• Historical Performance: Learns from past routing decisions

Integration with SynapticLlamas

SOLLOL is the load balancing engine that powers SynapticLlamas, a distributed multi-agent AI orchestration platform. While SOLLOL can be used standalone, SynapticLlamas adds:

• Multi-agent orchestration
• Collaborative workflows
• AST-based quality voting
• Interactive CLI
• Web dashboard

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

MIT License - see LICENSE file for details

Credits

Part of the SynapticLlamas project by BenevolentJoker-JohnL.