# Follow-up Post (Post 6-8 hours after initial)

Here's the SOLLOL dashboard in action 📊

[Attach 4 screenshots]

**What you're seeing:**

🟢 **Screenshot 1:** Node health monitoring
- 2 active Ollama nodes (192.168.1.21, 192.168.1.10)
- 11-12ms latency, 100% success rate
- 0% load, healthy status

⚙️ **Screenshot 2:** Active applications
- FlockParser (document processing)
- SynapticLlamas (multi-agent reasoning)
- Both connected via OllamaPool router

🎯 **Screenshot 3:** Real-time routing decisions
- Activity logs showing mxbai-embed-large requests/responses
- Latency tracking per request (163ms-2.3sec)
- llama.cpp activity stream connected
- Integrated Ray + Dask dashboards

📈 **Screenshot 4:** Dask distributed processing
- Task distribution across cluster workers
- Bytes stored: 299 MiB
- Processing + CPU + Data Transfer phases visible
- Task stream showing parallel execution

The adaptive routing automatically handles:
- Small batches (<100 texts) → Local ThreadPoolExecutor (lower overhead)
- Large batches (>100 texts) → Dask distributed (better parallelism)

**Key insight:** Connection pooling + HTTP/2 is where most of the speedup comes from. Dask adds another 1.4-2× on top for large batches.

Anyone else optimizing multi-node Ollama setups? Would love to compare notes.
