How to optimize Ollama performance and configure parameters? - 面试题

Optimizing Ollama performance can be approached from multiple aspects:

1. Model Quantization Selection: Ollama supports models with different quantization levels, balancing precision and performance:

q4_0 / q4_k_m - 4-bit quantization, fast speed, slightly lower precision
q5_0 / q5_k_m - 5-bit quantization, balanced speed and precision
q8_0 - 8-bit quantization, high precision, slower speed
f16 - 16-bit floating point, highest precision, slowest

bash
# Download different quantization versions
ollama pull llama3.1:8b-q4_k_m
ollama pull llama3.1:8b-q8_0

2. Parameter Tuning:

dockerfile
# Set in Modelfile
PARAMETER temperature 0.7      # Control randomness, 0-1
PARAMETER top_p 0.9           # Nucleus sampling, 0-1
PARAMETER top_k 40            # Number of sampling candidates
PARAMETER num_ctx 4096        # Context window size
PARAMETER repeat_penalty 1.1  # Repetition penalty
PARAMETER num_gpu 1           # Number of GPU layers

3. GPU Acceleration: Ensure GPU is properly configured:

bash
# Linux/macOS
export CUDA_VISIBLE_DEVICES=0

# Check GPU usage
nvidia-smi

4. Batch Processing Optimization:

bash
# Set batch size during API calls
curl http://localhost:11434/api/generate -d '{
  "model": "llama3.1",
  "prompt": "Hello",
  "options": {
    "num_batch": 512,
    "num_gpu": 99
  }
}'

5. Memory Management:

dockerfile
# Limit context length to reduce memory usage
PARAMETER num_ctx 2048

# Use smaller models
FROM llama3.1:8b  # instead of 70b

6. Concurrent Requests: Ollama supports processing multiple requests concurrently, but is limited by hardware resources. Can optimize by increasing the num_parallel parameter:

dockerfile
PARAMETER num_parallel 2

7. Caching Strategy: Ollama caches models and responses, first load is slower, subsequent requests will be faster.

Performance Monitoring:

bash
# View resource usage
ollama ps

# View logs
ollama logs