Three Types of LLM Workloads and How to Serve Them

Why It Matters

Understanding these workload nuances lets enterprises choose the right inference engine and infrastructure, reducing cost and meeting performance expectations in a rapidly maturing LLM market.

Key Takeaways

• Offline workloads prioritize throughput over latency.
• Online workloads require sub‑hundred‑millisecond response times.
• Semi‑online workloads need rapid autoscaling for burst traffic.
• vLLM excels at batch scheduling for high‑throughput jobs.
• SGLang reduces host overhead and supports speculative decoding.

Pulse Analysis

The rise of open‑source LLMs and inference engines is reshaping how companies deploy AI at scale. By moving away from opaque API pricing, firms can tailor inference pipelines to the specific characteristics of their workloads. Offline workloads—such as bulk data enrichment or video transcription—benefit from massive parallelism on GPUs, where engines like vLLM maximize throughput through mixed prefill‑decode batching and asynchronous RPC. This approach drives down per‑token cost and aligns with the economics of batch‑oriented data pipelines.

Online workloads, including conversational agents and code‑completion tools, demand ultra‑low latency to preserve user experience. Here, host‑side overhead becomes a bottleneck, and SGLang’s lightweight runtime coupled with speculative decoding (e.g., EAGLE‑3) can shave tens of milliseconds off response times. Leveraging tensor parallelism, FP8 quantization, and edge‑proximate HTTP proxies further compress the latency budget, enabling real‑time interaction without sacrificing model quality.

Semi‑online workloads sit between these extremes, experiencing sudden traffic spikes that require rapid scaling without incurring idle cost. Autoscaling GPU clusters, snapshot‑based cold‑start mitigation, and multitenant resource aggregation smooth peak‑to‑average ratios. By selecting the appropriate engine—vLLM for batch‑heavy phases or SGLang for latency‑sensitive bursts—organizations can maintain flexibility while controlling expenses. As LLM inference commoditizes, mastering these workload distinctions will be a competitive advantage for AI‑first enterprises.