February 2026 Summaries

Building reliable AI agents for production environments remains challenging despite increased accessibility, as complexities often lead to a "reliability ceiling" where advanced decision-making is required. Nebius and Toloka are integrating Tendem into the Nebius ecosystem to address these challenges by embedding vetted human experts into AI workflows, which allows for human judgment to be called upon through the Model Context Protocol (MCP) when AI agents encounter ambiguity. This integration provides a safety net that transforms expert judgment into a scalable, programmable reliability layer, improving task completion speed and quality while reducing errors that AI-only systems might encounter. With this move, Nebius aims to offer a comprehensive stack for AI developers, combining the intelligence of Token Factory, the autonomy of Tavily, and the reliability of human verification to create a seamless ecosystem capable of deploying trustworthy AI agents in high-stakes environments. This approach not only enhances AI performance but also aligns with enterprise requirements for governance and auditability, as emphasized by industry leaders from companies like Monday.com and Shopify.

Nebius Token Factory introduces Dedicated Endpoints, which offer granular deployment control for AI models, allowing teams to manage infrastructure variables such as GPU type, scaling limits, and regional data residency directly through a control plane API. This approach replaces the opacity of shared infrastructure with explicit configurations, enabling predictable latency and cost management. The integration of the Custom Weights Hub facilitates a seamless transition from post-training to deployment, allowing fine-tuned or distilled checkpoints to be deployed without tool-switching, thus supporting continuous iteration on models. This system operates on Nebius AI Cloud, using dedicated NVIDIA GPU clusters, and provides Inference Observability for real-time monitoring of latency, scaling behavior, and traffic patterns, ensuring that deployment decisions are informed by actual performance metrics. By integrating deployment as a core component of the production architecture, Nebius enables AI teams to transform model selection into system design, enhancing reliability, compliance, and efficiency at scale.

As AI's role in production grows, inference has emerged as a crucial operational challenge, with demands for continuous scalability directly affecting cost and user experience. NVIDIA emphasizes that throughput, latency, and cost per token are now core business metrics, as inference workloads have evolved to include combinations of large language models, embedding models, and task-specific models. Traditional GPU deployment methods, which dedicate full GPUs to individual models, lead to inefficiencies and rising costs. To address this, NVIDIA and Nebius conducted benchmarks using NVIDIA Run:ai on Nebius AI Cloud to test fractional GPU allocation. The results demonstrated improved efficiency and scalability for real-world inference workloads, with consistent throughput scaling, enhanced GPU utilization, stable latency, and reliable elastic autoscaling across multi-model workloads. This approach, utilizing dynamic workload scheduling and fractional GPU allocation, offers a more efficient model for production inference environments, reducing idle capacity and maintaining performance under high concurrency.

Running more than one vLLM replica in distributed inference workloads significantly impacts performance, particularly when considering routing strategies. A detailed experiment comparing cache-aware routing with default Kubernetes routing demonstrated a dramatic reduction in average inference step time, total runtime, and P95 latency when cache-aware routing was implemented. This improvement is attributed to better execution locality, allowing for the reuse of cached data and reducing the need for repeated prefills. While default Kubernetes routing does not consider request content or model state, the cache-aware approach preferentially routes requests to replicas that are more likely to have relevant context cached, enhancing efficiency. The experiment highlighted that scaling replicas without appropriate routing strategies can degrade performance due to cache fragmentation. Cache behavior, specifically KV cache reuse, emerged as the primary driver of performance differences, with cache-aware routing facilitating significantly higher cache usage. Although single-replica scenarios do not benefit from routing strategies, the role of routing becomes critical in distributed setups, where preserving locality across replicas is crucial for performance improvement. The study underscores the importance of routing in managing stateful workloads and suggests that while cache-aware routing is beneficial, further considerations such as memory pressure and cache eviction should be incorporated for optimal performance, which will be explored in future analyses.