KDnuggets Outlines 5 LLM Inference Projects to Cut Latency and Boost Throughput
Updated
Updated · KDnuggets · Oct 14
KDnuggets Outlines 5 LLM Inference Projects to Cut Latency and Boost Throughput
2 articles · Updated · KDnuggets · Oct 14
Summary
KDnuggets’ new guide lays out five hands-on projects to teach LLM inference engineering, from building a mini-vLLM to deploying a production-style inference API.
The projects target the core serving bottlenecks after training—latency, throughput, memory use and hardware efficiency—through techniques such as KV caching, continuous batching, quantization, speculative decoding and optimized attention kernels.
Concrete exercises include benchmarking FP16, INT8 and 4-bit models, writing a FlashAttention-style Triton kernel, and measuring metrics such as time to first token, tokens per second and draft-token acceptance rates.
The capstone combines caching, batching, quantization, streaming, load testing and routing into one service, aiming to show how tools like vLLM and SGLang work under real concurrency pressure.