LLM Inference Engineering
3 Topics
1
vLLM & PagedAttention
Self-hosting open-source LLMs, continuous batching, and CUDA memory management.
High - Required for any team building cost-sensitive AI products.
2
Quantization: GGUF, AWQ & EXL2
Model compression formats, precision trade-offs, and running 70B models on consumer hardware.
High - Essential for cost-efficient model deployment.
3
Prompt Caching & Speculative Decoding
Anthropic/Gemini prefix caching, 90% cost reduction, and speculative token generation.
High - Directly reduces costs and latency for production systems.