Tag: speculative decoding
2 reviews
The LLM inference engine playbook: tricks in vLLM and SGLang
Which optimization techniques do the vLLM and SGLang serving engines implement, and in what order should an engineer study them?
LLM serving engines are built from a stack of optimization tricks, each attacking one bottleneck: batching keeps the GPU busy, KV-cache paging and reuse stop memory from fragmenting or being recomputed, faster attention kernels and lower-precision arithmetic cut bytes moved, speculative decoding trades spare compute for fewer serial steps, and prefill-decode disaggregation lets two workloads with opposite resource profiles run on machines tuned for each. This review catalogs the trick list of the two dominant open-source engines, vLLM and SGLang, maps each trick to the paper that introduced it and the bottleneck it addresses, and closes with an ordered self-study path. Confidence is moderate: the mechanisms are well established in the literature, but the engines' own performance claims are vendor-reported and rarely independently benchmarked.
Optimizing large language model inference
Which techniques most improve the throughput, latency, and cost of large language model inference?
Large language model inference is memory-bound, not compute-bound, and every major optimization family attacks the memory wall from a different angle: batching and scheduling to keep GPUs busy, KV-cache management to shrink the state that grows with context, quantization to cut bytes per weight, speculative decoding to trade spare compute for fewer serial steps, and prefill-decode separation to stop two workloads with opposite resource profiles from interfering. The evidence is consistent that these techniques deliver large gains — commonly 2–6x in throughput or latency, occasionally 10x or more in narrow regimes — but nearly every headline number is an "up to" figure measured on the authors' own hardware and workload, and independent benchmarks are only beginning to appear.