High-Performance Model Serving & Inference Optimization

vLLM, PagedAttention, quantization, speculative decoding, and TensorRT-LLM.

3 weeks · wk 22–24Core / deep pace4 topics12 Interview Questions

Why this pace:Running open-source models at enterprise scale requires specialized inference servers capable of continuous batching, memory page management, and kernel fusion to minimize latency and maximize throughput.

SEC FOCUS

Protect model weights and API endpoints behind TLS termination, mutual authentication (mTLS), and DDoS throttling to prevent model extraction attacks.

Topics & Action Items (4)

Inference Engines: vLLM, PagedAttention & Continuous Batching

6 hrs

Weight & Activation Quantization: AWQ, GPTQ & GGUF

5 hrs

Speculative Decoding & Medusa Multi-Head Verification

5 hrs

TensorRT-LLM & Hardware-Specific Kernel Optimizations

5 hrs

Previous Stage

Full-Stack AI Application Architecture

Next Stage

Distributed AI Systems & GPU Infrastructure