Comparison of Local Deployment Options for Large Models

Why Local Deployment Is Needed

Local deployment of large models offers three major advantages: Data security—data stays local, meeting compliance requirements; Cost control—no API call fees, suitable for high-frequency usage; Low latency—no network round trips, faster responses. However, it also requires hardware costs and maintenance efforts.

Ollama: The Simplest One-Click Deployment

Ollama is the easiest-to-use local large model deployment tool, supporting macOS, Linux, and Windows:

# Install Ollama
curl -fsSL https://ollama.com/install.sh | sh

# Pull and run a model
ollama run deepseek-r1:7b
ollama run qwen2.5:7b
ollama run llama3.1:8b

# API call
curl http://localhost:11434/api/generate -d '{
  "model": "deepseek-r1:7b",
  "prompt": "Explain what quantum computing is",
  "stream": false
}'

Pros: Simple installation, friendly commands, automatic model file management, built-in REST API. Cons: Performance not as good as vLLM, no multi-GPU support, limited concurrency.

llama.cpp: The King of CPU Inference

llama.cpp is a high-performance inference engine implemented in pure C/C++, supporting hybrid CPU and GPU inference:

# Compile
make -j

# Run model (GGUF format)
./llama-cli -m model.gguf -p "Hello, please introduce yourself" -n 512

# Server mode
./llama-server -m model.gguf --port 8080

Pros: Excellent CPU inference performance, supports quantized models, low memory usage, cross-platform. Cons: No tensor parallelism, relatively basic features.

vLLM: Production-Grade High-Performance Inference

vLLM is currently the most powerful open-source inference engine, designed for high throughput, supporting PagedAttention and continuous batching:

# Install
pip install vllm

# Start API server
python -m vllm.entrypoints.openai.api_server \
  --model deepseek-ai/deepseek-llm-7b-chat \
  --tensor-parallel-size 2 \
  --max-model-len 8192

# API call
from openai import OpenAI
client = OpenAI(base_url="http://localhost:8000/v1")
response = client.chat.completions.create(
    model="deepseek-ai/deepseek-llm-7b-chat",
    messages=[{"role": "user", "content": "Hello"}]
)

Pros: Highest throughput, supports multi-GPU tensor parallelism, OpenAI API compatible, PagedAttention optimizes memory. Cons: Relatively complex configuration, requires GPU, slower startup.

Comparison of Solutions

Selection Recommendations

• Personal development/learning: Ollama
• CPU servers: llama.cpp
• GPU production environments: vLLM
• Low-resource devices: llama.cpp + GGUF quantized models