⚙️Engineering
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LLM Inference Optimization
Optimize LLM inference for speed, cost, and efficiency
Prerequisites
🌱
Beginner
BeginnerInference optimization basics
What to Learn
- •Understanding inference bottlenecks
- •Quantization fundamentals (INT8, INT4)
- •KV cache and memory management
- •Batching for throughput
- •Using vLLM and TGI
Resources
- 📚vLLM documentation
- 📚HuggingFace TGI guide
- 📚Quantization tutorials
🌿
Intermediate
IntermediateProduction inference systems
What to Learn
- •PagedAttention and continuous batching
- •Speculative decoding
- •Tensor parallelism and model sharding
- •GPTQ, AWQ, and GGUF formats
- •Inference frameworks comparison
Resources
- 📚vLLM paper
- 📚Speculative decoding paper
- 📚Llama.cpp and GGUF docs
🌳
Advanced
AdvancedCutting-edge optimization research
What to Learn
- •Custom CUDA kernels for inference
- •Sparse attention implementations
- •Model compression research
- •Hardware-aware optimization
- •Cost modeling and optimization
Resources
- 📚Flash Attention implementation
- 📚CUDA programming for ML
- 📚Hardware optimization papers