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Large language model (LLM) inference has become a cornerstone of the technological strategy for many organizations. However, serving these models efficiently poses complex challenges, especially when aiming to minimize latency and maximize throughput in production environments. One of the most advanced architectures to achieve this is the disaggregation of the prefill and decode phases, which assigns each stage to separate GPU groups to avoid interference. Nevertheless, even with this separation, a new type of asymmetry arises: bursty and heavy-tailed workloads can saturate prefill nodes while decode nodes remain underutilized. This imbalance causes queuing time and key-value transfer between nodes to become the main contributors to time-to-first-token (TTFT).
Recent research proposes a proactive approach: a scheduler that diverts prefill requests to decode nodes when the decode path offers lower latency. Instead of passively waiting in a queue, the system evaluates for each request what its TTFT would be on the prefill node and on each decode node, seeking the optimal chunk size that does not affect the inter-token interval of ongoing decodings. By executing the prefill directly on the decode node, the KV-cache transfer between nodes is eliminated, drastically reducing latency. Implemented on vLLM and tested with real traces from DeepSeek-V2-Lite, this approach achieves up to an 81% reduction in P95 TTFT and improves SLO compliance by 79% compared to traditional disaggregated schedulers, with a routing cost of less than one millisecond per request.
This type of solution illustrates how optimizing LLM inference requires not only powerful hardware but also intelligent software that dynamically adapts to load conditions. For companies looking to integrate artificial intelligence into their processes, having a technology partner that understands these complexities is essential. At Q2BSTUDIO, we develop custom applications that incorporate state-of-the-art language models, optimizing their deployment in hybrid cloud and multi-cloud environments. We work with AWS and Azure cloud services to deploy scalable infrastructures that guarantee low latency and high availability, which is critical in conversational AI systems, virtual assistants, or recommendation engines.
Additionally, we offer custom software solutions that integrate advanced artificial intelligence components, such as autonomous AI agents capable of interacting with corporate knowledge bases, automating complex workflows, and adapting to unpredictable usage patterns. Our team also implements Power BI dashboards to monitor real-time performance metrics of these systems and applies cybersecurity strategies to protect the models and sensitive data they process. If your company seeks to deploy AI for businesses efficiently, at Q2BSTUDIO we combine expertise in software engineering, cloud, and artificial intelligence to build robust and personalized solutions.
