Top 6 LLM Inference Runtimes: 2025 Review

In 2025, the bottleneck for large language models has shifted from training to serving. The speed and cost of delivering tokens in real‑world traffic hinge on three low‑level implementation choices that inference runtimes make: how they batch requests, how they overlap the prefill stage with the decode stage, and how they store and reuse the key‑value (KV) cache. The MarkTechPost piece titled "Comparing the Top 6 Inference Runtimes for LLM Serving in 2025" reviews six leading engines—such as FasterTransformer, Triton Inference Server, and NVIDIA's NeMo, among others—analyzing each of these dimensions in depth. By dissecting the trade‑offs that each runtime embraces, the article provides a clear roadmap for engineers looking to choose the right stack for their production workloads.

The evaluation shows that engines that aggressively batch small requests achieve the lowest latency per token, but at the cost of increased memory pressure. Conversely, runtimes that prioritize prefill‑decode overlap reduce the number of kernel launches and can sustain higher throughput on GPUs with limited memory bandwidth. KV cache reuse emerges as the most powerful lever for reducing compute; runtimes that de‑duplicate or compress the cache can cut inference FLOPs by up to 30 %. For example, NVIDIA’s NeMo with its built‑in KV cache deduplication outperforms a vanilla Triton deployment in both latency and cost when serving a mixed workload of short and long prompts.

The takeaway for practitioners is that no single runtime dominates across all metrics; the optimal choice depends on the volume and variability of traffic, the GPU infrastructure, and the cost model. If your service handles many short prompts, a highly batched engine with aggressive KV compression will deliver the best cost‑per‑token. For workloads with long, continuous decoding, a runtime that overlaps prefill and decode and keeps the KV cache resident will shine. The article equips teams with the data needed to make an evidence‑based decision and to tune their inference pipelines for the demanding traffic patterns of 2025.