Deepseek Deepseek Coder

DeepSeek DeepSeek-Coder — run DeepSeek-V3, DeepSeek-R1, DeepSeek-Coder across your local fleet. 7-signal scoring routes every request to the best device. Cross-platform (macOS, Linux, Windows). Zero cloud costs via Ollama Herd.

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

bash

pip install ollama-herd
herd              # start the router (port 11435)
herd-node         # run on each machine

Package: ollama-herd | Repo: github.com/geeks-accelerator/ollama-herd

Models are pulled on demand — the router auto-pulls when a request arrives for a model not yet on any node, or you can pull manually via the dashboard. No models are downloaded during installation.

Use DeepSeek through the fleet

OpenAI SDK

python

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

python

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

bash

# 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 (optional — choose models that fit your RAM)

Cross-platform: These are example configurations. Any device (Mac, Linux, Windows) with equivalent RAM works. The fleet router runs on all platforms.

DeepSeek offers models at every size. Pick the one that fits your available memory — smaller models work great for most tasks:

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_ctx changes
• 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

bash

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

bash

curl http://localhost:11435/api/transcribe -F "audio=@recording.wav"

Embeddings

bash

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

Agent Setup Guide

Guardrails

• Model downloads require explicit user confirmation — DeepSeek models range from 1GB (1.5B) to 400GB+ (671B). Always confirm before pulling.
• Model deletion requires explicit user confirmation — never remove models without asking.
• Never delete or modify files in ~/.fleet-manager/.
• If a DeepSeek model is too large for available memory, suggest a smaller variant (e.g., deepseek-r1:7b instead of :70b).
• No models are downloaded automatically — all pulls are user-initiated or require opt-in via the auto_pull setting.