Efficient Inference Optimization
Optimize AI inference for speed and cost: batching, caching, model serving, KV cache, speculative decoding, and more.
TL;DR
Optimize inference with: dynamic batching, KV cache, speculative decoding, quantization, and efficient serving frameworks (vLLM, TGI). Achieve 2-10x speedup.
Dynamic batching
Group multiple requests together:
- Process in parallel
- Better GPU utilization
- Higher throughput
KV cache
Cache attention key-value pairs:
- Avoid recomputing for each token
- Critical for autoregressive generation
- Trade memory for speed
Speculative decoding
- Use small "draft" model to generate multiple tokens
- Large model verifies in parallel
- 2-3x speedup with no quality loss
Serving frameworks
vLLM: Optimized LLM serving (PagedAttention)
TGI (Text Generation Inference): Hugging Face's server
TensorRT-LLM: Nvidia optimization
Ollama: Local serving made easy
Optimization techniques
- Flash Attention (memory-efficient attention)
- Mixed precision (FP16/BF16)
- Quantization (INT8)
- Kernel fusion (combine ops)
Monitoring
- Latency (p50, p95, p99)
- Throughput (requests/sec)
- GPU utilization
- Memory usage
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Key Terms Used in This Guide
Inference
When a trained AI model processes new input and generates a prediction or responseāthe 'using' phase after training is done.
Model
The trained AI system that contains all the patterns it learned from data. Think of it as the 'brain' that makes predictions or decisions.
AI (Artificial Intelligence)
Making machines perform tasks that typically require human intelligenceālike understanding language, recognizing patterns, or making decisions.
Latency
How long it takes for an AI model to generate a response after you send a request.
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