- name
- deepseek
- description
- DeepSeek models on your local fleet — DeepSeek-V3, DeepSeek-V3.2, DeepSeek-R1, DeepSeek-Coder routed across multiple devices via Ollama Herd. 7-signal scoring picks the best machine for every request. Run DeepSeek locally on Apple Silicon with zero cloud costs.
- version
- 1.0.0
- homepage
- https://github.com/geeks-accelerator/ollama-herd
- metadata
- {"openclaw":{"emoji":"brain","requires":{"anyBins":["curl","wget"],"optionalBins":["python3","pip"]},"configPaths":["~/.fleet-manager/latency.db","~/.fleet-manager/logs/herd.jsonl"],"os":["darwin","linux"]}}
DeepSeek — Run DeepSeek Models Across Your Local Fleet
Run DeepSeek-V3, DeepSeek-R1, and DeepSeek-Coder on your own hardware. The fleet router picks the best device for every request — no cloud API needed, zero per-token costs, all data stays on your machines.
Supported DeepSeek models
| Model | Parameters | Ollama name | Best for |
|---|---|---|---|
| DeepSeek-V3 | 671B MoE (37B active) | deepseek-v3 | General — matches GPT-4o on most benchmarks |
| DeepSeek-V3.1 | 671B MoE | deepseek-v3.1 | Hybrid thinking/non-thinking modes |
| DeepSeek-V3.2 | 671B MoE | deepseek-v3.2 | Improved reasoning + agent performance |
| DeepSeek-R1 | 1.5B–671B | deepseek-r1 | Reasoning — approaches O3 and Gemini 2.5 Pro |
| DeepSeek-Coder | 1.3B–33B | deepseek-coder | Code generation (87% code, 13% NL training) |
| DeepSeek-Coder-V2 | 236B MoE (21B active) | deepseek-coder-v2 | Code — matches GPT-4 Turbo on code tasks |
Setup
pip install ollama-herd
herd # start the router (port 11435)
herd-node # run on each machine
# Pull a DeepSeek model
ollama pull deepseek-r1:70bPackage: ollama-herd | Repo: github.com/geeks-accelerator/ollama-herd
Use DeepSeek through the fleet
OpenAI SDK
from openai import OpenAI
client = OpenAI(base_url="http://localhost:11435/v1", api_key="not-needed")
# DeepSeek-R1 for reasoning
response = client.chat.completions.create(
model="deepseek-r1:70b",
messages=[{"role": "user", "content": "Prove that there are infinitely many primes"}],
stream=True,
)
for chunk in response:
print(chunk.choices[0].delta.content or "", end="")DeepSeek-Coder for code
response = client.chat.completions.create(
model="deepseek-coder-v2:16b",
messages=[{"role": "user", "content": "Write a Redis cache decorator in Python"}],
)
print(response.choices[0].message.content)Ollama API
# DeepSeek-V3 general chat
curl http://localhost:11435/api/chat -d '{
"model": "deepseek-v3",
"messages": [{"role": "user", "content": "Explain quantum computing"}],
"stream": false
}'
# DeepSeek-R1 reasoning
curl http://localhost:11435/api/chat -d '{
"model": "deepseek-r1:70b",
"messages": [{"role": "user", "content": "Solve this step by step: ..."}],
"stream": false
}'Hardware recommendations
DeepSeek models are large. Here's what fits where:
| Model | Min RAM | Recommended hardware |
|---|---|---|
deepseek-r1:1.5b | 4GB | Any Mac |
deepseek-r1:7b | 8GB | Mac Mini M4 (16GB) |
deepseek-r1:14b | 12GB | Mac Mini M4 (24GB) |
deepseek-r1:32b | 24GB | Mac Mini M4 Pro (48GB) |
deepseek-r1:70b | 48GB | Mac Studio M4 Max (128GB) |
deepseek-coder-v2:16b | 12GB | Mac Mini M4 (24GB) |
deepseek-v3 | 256GB+ | Mac Studio M3 Ultra (512GB) |
The fleet router automatically sends requests to the machine where the model is loaded — no manual routing needed.
Why run DeepSeek locally
- Zero cost — DeepSeek API charges per token. Local is free after hardware.
- Privacy — code and business data never leave your network.
- No rate limits — DeepSeek API throttles during peak hours. Local has no throttle.
- Availability — DeepSeek API has had outages. Your hardware doesn't depend on their servers.
- Fleet routing — multiple machines share the load. One busy? Request goes to the next.
Fleet features
- 7-signal scoring — picks the optimal node for every request
- Auto-retry — fails over to next best node transparently
- VRAM-aware fallback — routes to a loaded model in the same category instead of cold-loading
- Context protection — prevents expensive model reloads from
num_ctxchanges - Request tagging — track per-project DeepSeek usage
Also available on this fleet
Other LLM models
Llama 3.3, Qwen 3.5, Phi 4, Mistral, Gemma 3 — any Ollama model routes through the same endpoint.
Image generation
curl -o image.png http://localhost:11435/api/generate-image \
-H "Content-Type: application/json" \
-d '{"model":"z-image-turbo","prompt":"a sunset","width":1024,"height":1024,"steps":4}'Speech-to-text
curl http://localhost:11435/api/transcribe -F "audio=@recording.wav"Embeddings
curl http://localhost:11435/api/embeddings -d '{"model":"nomic-embed-text","prompt":"query"}'Dashboard
http://localhost:11435/dashboard — monitor DeepSeek requests alongside all other models. Per-model latency, token throughput, health checks.
Full documentation
Guardrails
- Never pull or delete DeepSeek models without user confirmation — downloads are 4-400+ GB.
- Never delete or modify files in
~/.fleet-manager/. - If a DeepSeek model is too large for available memory, suggest a smaller variant.