Ollama Herd Fleet Manager#

You are managing an Ollama Herd fleet — a smart Ollama multimodal router that distributes Ollama AI workloads across multiple devices. Ollama Herd handles 4 model types: Ollama LLM inference, image generation (mflux), speech-to-text (Qwen3-ASR), and Ollama embeddings. The Ollama scoring engine evaluates nodes on 7 signals (thermal state, memory fit, queue depth, latency history, role affinity, availability trend, context fit) and routes each Ollama request to the optimal device.

Install Ollama Herd#

bash

pip install ollama-herd          # install Ollama Herd from PyPI
herd                             # start the Ollama router
herd-node                        # start an Ollama node agent (run on each device)

PyPI: ollama-herd | Source: github.com/geeks-accelerator/ollama-herd

Ollama Router endpoint#

The Ollama Herd router runs at http://localhost:11435 by default. If the user has specified a different Ollama URL, use that instead.

Ollama API endpoints#

Use curl to interact with the Ollama fleet:

Ollama fleet status — overview of all Ollama nodes and queues#

bash

# ollama_fleet_status — check Ollama node health
curl -s http://localhost:11435/fleet/status | python3 -m json.tool

Returns:

• fleet.nodes_total / fleet.nodes_online — how many Ollama devices are in the fleet
• fleet.models_loaded — total Ollama models currently loaded across all nodes
• fleet.requests_active — total in-flight Ollama requests
• nodes[] — per-node details: Ollama status, hardware, memory, CPU, disk, loaded Ollama models with context lengths
• queues — per Ollama node:model queue depths (pending, in-flight, done, failed)

List all Ollama models available across the fleet#

bash

# ollama_model_list — all Ollama models on all nodes
curl -s http://localhost:11435/api/tags | python3 -m json.tool

Pull an Ollama model onto the fleet#

bash

# ollama_pull_model — pull a model (auto-selects best node, streams progress)
curl -N http://localhost:11435/api/pull -d '{"name": "codestral"}'

# pull to a specific node
curl -N http://localhost:11435/api/pull -d '{"name": "llama3.3:70b", "node_id": "mac-studio"}'

# non-streaming (blocks until complete)
curl http://localhost:11435/api/pull -d '{"name": "phi4", "stream": false}'

List Ollama models currently loaded in memory#

bash

# ollama_loaded_models — hot Ollama models in GPU memory
curl -s http://localhost:11435/api/ps | python3 -m json.tool

OpenAI-compatible Ollama model list#

bash

curl -s http://localhost:11435/v1/models | python3 -m json.tool

Ollama usage statistics (per-node, per-model daily aggregates)#

bash

curl -s http://localhost:11435/dashboard/api/usage | python3 -m json.tool

Recent Ollama request traces#

bash

# ollama_traces — recent Ollama routing decisions
curl -s "http://localhost:11435/dashboard/api/traces?limit=20" | python3 -m json.tool

Returns the last N Ollama routing decisions with: model requested, node selected, score, latency, tokens, retry/fallback status, tags.

Ollama fleet health analysis#

bash

curl -s http://localhost:11435/dashboard/api/health | python3 -m json.tool

Returns 15 automated Ollama health checks: offline/degraded nodes, memory pressure, underutilized nodes, VRAM fallbacks, KV cache bloat (OLLAMA_NUM_PARALLEL too high), version mismatch, context protection, zombie reaper, Ollama model thrashing, request timeouts, error rates, retry rates, client disconnects, and incomplete streams.

Ollama model recommendations#

bash

curl -s http://localhost:11435/dashboard/api/recommendations | python3 -m json.tool

Returns AI-powered Ollama model mix recommendations per node based on hardware capabilities, Ollama usage patterns, and curated benchmark data.

Ollama settings#

bash

# View current Ollama config and node versions
curl -s http://localhost:11435/dashboard/api/settings | python3 -m json.tool

# Toggle Ollama runtime settings (auto_pull, vram_fallback)
curl -s -X POST http://localhost:11435/dashboard/api/settings \
  -H "Content-Type: application/json" \
  -d '{"auto_pull": false}'

Ollama model management#

bash

# View per-node Ollama model details with sizes and usage
curl -s http://localhost:11435/dashboard/api/model-management | python3 -m json.tool

# Pull an Ollama model onto a specific node
curl -s -X POST http://localhost:11435/dashboard/api/pull \
  -H "Content-Type: application/json" \
  -d '{"model": "llama3.3:70b", "node_id": "mac-studio"}'

# Delete an Ollama model from a specific node
curl -s -X POST http://localhost:11435/dashboard/api/delete \
  -H "Content-Type: application/json" \
  -d '{"model": "old-model:7b", "node_id": "mac-studio"}'

Ollama model insights (summary statistics)#

bash

curl -s http://localhost:11435/dashboard/api/models | python3 -m json.tool

Per-app Ollama analytics (requires request tagging)#

bash

curl -s http://localhost:11435/dashboard/api/apps | python3 -m json.tool

Ollama Dashboard#

The Ollama web dashboard is at http://localhost:11435/dashboard. It has eight tabs:

• Fleet Overview — live Ollama node cards, queue depths, and request counts via SSE
• Trends — Ollama requests per hour, average latency, and token throughput charts (24h–7d)
• Model Insights — per-Ollama-model latency, tokens/sec, usage comparison
• Apps — per-tag Ollama analytics with request volume, latency, tokens, error rates
• Benchmarks — Ollama capacity growth over time with per-run throughput and latency percentiles
• Health — 15 automated Ollama fleet health checks with severity levels
• Recommendations — Ollama model mix recommendations per node with one-click pull
• Settings — Ollama runtime toggle switches, read-only config tables, and node version tracking

Direct the user to open this URL in their browser for visual Ollama monitoring.

Ollama Resilience features#

• Auto-retry — if an Ollama node fails before the first response chunk, re-scores and retries on the next-best Ollama node (up to 2 retries)
• Ollama model fallbacks — clients specify backup Ollama models; tries alternatives when the primary is unavailable
• Context protection — strips num_ctx from Ollama requests when unnecessary to prevent Ollama model reload hangs; auto-upgrades to a larger loaded model
• VRAM-aware fallback — routes to an already-loaded Ollama model in the same category instead of cold-loading
• Zombie reaper — background task detects and cleans up stuck in-flight Ollama requests
• Auto-pull — automatically pulls missing Ollama models onto the best available node

Common Ollama tasks#

Check if the Ollama fleet is healthy#

1. Hit /fleet/status and verify nodes_online > 0
2. Hit /dashboard/api/health for automated Ollama health checks with severity levels
3. Look at Ollama queue depths — deep queues may indicate a bottleneck

Find which Ollama node has a specific model#

1. Hit /fleet/status and inspect each Ollama node's ollama.models_loaded and ollama.models_available
2. Or hit /api/tags for a flat list of all available Ollama models with which nodes have them

Check if an Ollama model is loaded (hot) or cold#

1. Hit /api/ps — Ollama models listed here are currently loaded in memory (hot)
2. Models in /api/tags but not in /api/ps are on disk but not loaded (cold)

View recent Ollama inference activity#

1. Hit /dashboard/api/traces?limit=10 to see the last 10 Ollama requests
2. Each trace shows: Ollama model, node, score, latency, tokens, retry/fallback status

Diagnose slow Ollama responses#

1. Check /dashboard/api/traces for high latency Ollama entries
2. Check /fleet/status for Ollama nodes with high queue depths or memory pressure
3. Check if the Ollama model had to cold-load (look for low scores in trace)
4. Check if num_ctx is being sent — Ollama context protection logs show if requests triggered reloads

Query the Ollama trace database directly#

bash

# Recent Ollama failures
sqlite3 ~/.fleet-manager/latency.db "SELECT request_id, model, status, error_message FROM request_traces WHERE status='failed' ORDER BY timestamp DESC LIMIT 10"

# Slowest Ollama requests
sqlite3 ~/.fleet-manager/latency.db "SELECT model, node_id, latency_ms/1000.0 as secs FROM request_traces WHERE status='completed' ORDER BY latency_ms DESC LIMIT 10"

Test Ollama inference through the fleet#

bash

# Ollama via OpenAI format
curl -s http://localhost:11435/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model":"llama3.3:70b","messages":[{"role":"user","content":"Hello from Ollama"}],"stream":false}'

# Ollama native format
curl -s http://localhost:11435/api/chat \
  -d '{"model":"llama3.3:70b","messages":[{"role":"user","content":"Hello from Ollama"}],"stream":false}'

Ollama Guardrails#

• Never restart or stop the Ollama Herd router or Ollama node agents without explicit user confirmation.
• Never delete or modify files in ~/.fleet-manager/ (contains Ollama latency data, traces, and logs).
• Do not pull Ollama models onto nodes without user confirmation — Ollama model downloads can be large (10-100+ GB).
• Do not delete Ollama models without user confirmation.
• If an Ollama node shows as offline, report it to the user rather than attempting to SSH into the machine.

Ollama Failure handling#

• If curl to the Ollama router fails with connection refused, tell the user the Ollama Herd router may not be running and suggest herd to start it.
• If the Ollama fleet status shows 0 nodes online, suggest starting Ollama node agents with herd-node on their devices.
• If Ollama mDNS discovery fails, suggest using --router-url http://router-ip:11435 for explicit connection.
• If Ollama requests hang with 0 bytes returned, check if the client is sending num_ctx — Ollama context protection should strip it.
• If a specific Ollama API endpoint returns an error, show the user the full error response and suggest checking the Ollama JSONL logs at ~/.fleet-manager/logs/herd.jsonl.