DeepSeek — DeepSeek-V3, DeepSeek-R1, DeepSeek-Coder on Your Local Devices

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-r1:70b

Package: 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

Fleet features

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

Agent Setup Guide

Guardrails

pip install ollama-herd
herd              # start the router (port 11435)
herd-node         # run on each machine
Pull a DeepSeek model
ollama pull deepseek-r1:70b

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