# SOLLOL 🌟 — A Production-Grade Orchestration & Observability Layer for Ollama / Llama.cpp Nodes

Hey everyone — I've been developing **SOLLOL**, a **production-grade orchestration and observability layer** for distributed Ollama and Llama.cpp infrastructures.

It consolidates orchestration patterns I first built in two other projects — **FlockParser** and **SynapticLlamas** — which now *prove SOLLOL works at scale*.

## 🔹 Why SOLLOL Exists
I built FlockParser and SynapticLlamas to test distributed embedding and agent coordination.
After seeing consistent 30–60× speed improvements across nodes, I extracted the orchestration logic into a reusable system: **SOLLOL**.

## 🌟 Key Features

* Distributed orchestration using Dask
* Live node & queue monitoring
* Adaptive batching for parallel embeddings
* Smart connection pooling & caching
* Multi-node GPU/CPU scheduling
* Integrated performance visualization dashboard

## ✅ Proven in Production Via:

* **FlockParser:** parallel embedding pipeline (61× speedup)
* **SynapticLlamas:** distributed multi-agent reasoning proof-of-concept

**Current Throughput:** 19–21 embeddings/sec using 3 nodes (1 GPU, 2 CPU)

## Questions for the Ollama Team:

1. Does Ollama reuse connections between embedding requests?
2. Any internal queue depth or batching constraints we should tune for?
3. Would persistent sessions or socket pooling improve performance?
4. Any plans for embedding stream or async APIs?
5. Recommendations for distributed concurrency best practices?

## Repos:

* 🌟 [SOLLOL](https://github.com/BenevolentJoker-JohnL/SOLLOL)
* ⚙️ [FlockParser](https://github.com/BenevolentJoker-JohnL/FlockParser)
* 🧠 [SynapticLlamas](https://github.com/BenevolentJoker-JohnL/SynapticLlamas)

Would love feedback or insights from anyone running multi-node or distributed Ollama setups.
