3.5 KiB
Ollama — Local AI Reference
Ollama runs on Mike's workstation (DESKTOP-0O8A1RL) with GPU acceleration. Available to all team members via Tailscale.
Models
| Model | Size | Use For |
|---|---|---|
qwen3:14b |
9.3 GB | Summarization, classification, data extraction, drafting |
codestral:22b |
12 GB | Code generation, refactoring suggestions, docstrings |
nomic-embed-text |
274 MB | Embeddings only (used by GrepAI) |
Endpoints
Auto-detect: any machine that has a local Ollama listening on 127.0.0.1:11434 uses local. Otherwise fall back to Mike's workstation over Tailscale.
# Preferred universal resolver — works on any machine
if curl -s -m 2 http://localhost:11434/api/tags >/dev/null 2>&1; then
OLLAMA="http://localhost:11434"
else
OLLAMA="http://100.92.127.64:11434"
fi
Rationale:
- Mike's workstation (DESKTOP-0O8A1RL): local matches, no change.
- HOWARD-HOME: also has a local Ollama with the canonical model set (confirmed 2026-04-22). Uses local — faster, zero Tailscale hop, no load on Mike's GPU.
- Other team machines: no local Ollama → falls back to Mike's over Tailscale.
- Mike's machine offline: graceful degradation — local users continue working; non-local users get a clean timeout.
Manual override (for testing or explicit preference): set OLLAMA=http://100.92.127.64:11434 before the call.
Check reachability:
curl -s $OLLAMA/api/tags | jq -r '.models[].name'
If neither endpoint responds: verify Tailscale (tailscale status) and whether your local Ollama service is running.
Access Control
- Port 11434 allowed ONLY from Tailscale subnet (100.0.0.0/8)
- NOT exposed to LAN, VPN, or internet
- Binding:
OLLAMA_HOST=0.0.0.0:11434(firewall restricts)
Calling Ollama
Use the /api/chat endpoint with think:false for qwen3 models. The older /api/generate endpoint on qwen3 puts output into thinking tokens that don't appear in the response field — you'll get an empty response if you use /api/generate.
Preferred one-liner:
python -c "
import urllib.request, json, sys, os
OLLAMA = os.environ.get('OLLAMA') or ('http://localhost:11434' if __import__('urllib.request').request.urlopen(urllib.request.Request('http://localhost:11434/api/tags'),timeout=2) else 'http://100.92.127.64:11434')
body = json.dumps({
'model':'qwen3:14b',
'messages':[{'role':'user','content': sys.argv[1]}],
'stream':False,
'think':False
}).encode()
res = json.loads(urllib.request.urlopen(urllib.request.Request(OLLAMA+'/api/chat', body), timeout=120).read())
print(res['message']['content'])
" "Your prompt here"
Or set $OLLAMA once from bash (see auto-detect formula above) and reuse it across calls.
For code suggestions, swap qwen3:14b for codestral:22b. Codestral doesn't need think:false.
Cold-start is ~30-50s on first call per model per session. Warm calls are 1-5s.
When to Use Which Model
| Task | Model |
|---|---|
| Summarize logs, diffs, session notes | qwen3:14b |
| Classify bug type, severity, category | qwen3:14b |
| Extract structured data from text | qwen3:14b |
| Draft commit message from diff | qwen3:14b |
| Suggest refactor for a function | codestral:22b |
| Docstring / comment generation | codestral:22b |
Review Policy
- Low-stakes output (summary, classification, draft) — use directly
- Code suggestions from codestral — always review before applying
- Never use Ollama for: auth decisions, credential handling, production migrations, security review