Performance improvement
Q: What are the techniques to improve performance? To optimize model performance, consider the following techniques:- Quantization
- Check model type: Determine whether the model is GQA (Grouped Query Attention) or MQA (Multi-Query Attention).
- Increase batch size to improve throughput.
Benchmarking
Q: How can we benchmark? There are multiple ways to benchmark your deployment’s performance:- Use our open-source load-testing tool
- Develop custom performance testing scripts
- Integrate with monitoring tools to track metrics
Model latency
Q: What’s the latency for small, medium, and large LLM models? Model latency and performance depend on various factors:- Input/output prompt lengths
- Model quantization
- Model sharding
- Disaggregated prefill processes
- Hardware configuration
- Multiple layers of caching
- Fire optimizations
- LoRA adapters (Low-Rank Adaptation)
Performance factors
Q: What factors affect model latency and performance? Key factors that impact latency and performance include:- Model architecture and size
- Hardware configuration
- Network conditions
- Request patterns
- Batch size settings
- Caching implementation
Best practices
Q: What are the best practices for optimizing performance? For optimal performance, follow these recommendations:- Choose an appropriate model size for your specific use case.
- Implement batching strategies to improve efficiency.
- Use quantization where applicable to reduce computational load.
- Monitor and adjust scaling parameters to meet demand.
- Optimize prompt lengths to reduce processing time.
- Implement caching to minimize repeated calculations.
Additional resources
- Discord Community: discord.gg/fireworks-ai
- Email Support: inquiries@fireworks.ai
- Documentation: Fireworks.ai docs