February 2026 Summaries

Distributed caches in large-scale systems often face the NxM problem, where the number of connections between clients and cache nodes grows multiplicatively, leading to potential issues as systems scale. This problem emerges when each client opens connections to every node in a cluster, causing a significant increase in connection load during scaling events. As clients and nodes increase, connection storms can occur, impacting system performance by consuming resources for connection management rather than serving requests. Mitigation strategies include staggered rollouts, connection pooling, and treating connection count as a critical capacity metric, though these solutions shift complexity rather than eliminate the problem. The NxM challenge signifies a system's growth and necessitates proactive capacity planning to prevent incidents during scaling operations. Understanding and addressing this issue is crucial for maintaining performance and reliability, especially as systems continue to grow in size and complexity.

In a large cache system, performance bottlenecks often arise not from the cache load itself but from the overhead of managing numerous TCP connections, especially when many application instances maintain their own connections to cache nodes, leading to CPU resource consumption on tasks like TLS negotiation and connection tracking. Introducing a routing layer between clients and cache nodes can alleviate these issues by multiplexing connections, thus reducing the number of direct connections each client has to make with cache nodes and minimizing CPU overhead on connection management. By transitioning from a direct client-to-cluster architecture to a hub-and-spoke model, routing layers centralize and manage connections more efficiently, allowing backend nodes to focus resources on request processing rather than infrastructure overhead. Advanced implementations, like those by Twitter and Facebook, have demonstrated the value of this approach by pushing connection complexity out of cache nodes, although the quality of execution is crucial, as poor implementations can introduce new bottlenecks. Moreover, routing layers can also cache frequently accessed data, absorbing significant traffic and reducing backend load, leading to improved latency performance. While routing layers offer substantial benefits in terms of connection management and load distribution, they introduce additional complexity that requires careful monitoring and engineering expertise to ensure that they do not themselves become performance bottlenecks.

The Unlocked Conference showcased the unique scaling challenges faced by massive companies like Uber, Apple, and Mercado Libre, highlighting how similar infrastructure and caching dependencies can yield vastly different problems at scale. Uber's lesson focused on coordination issues arising from aggressive client reconnections during system slowdowns, which led to connection storms and overwhelmed nodes, necessitating updates to client libraries and retry behaviors. Apple's discussion centered on how increased payload sizes in their systems led to significant latency spikes, emphasizing the importance of visibility into payload characteristics and the improvements they implemented in their Valkey system to mitigate these issues. Mercado Libre's experience highlighted the inefficiencies and risks introduced by shared infrastructure, where bandwidth limitations and inefficient data movement across numerous internal services created significant challenges, prompting the implementation of governance tools and workload management strategies. These varied experiences underline that scaling issues often arise not from the architecture itself but from the specific pressures and assumptions embedded within each system's unique design and operational context.

In an exploration of performance optimization for large-scale inference clusters, the integration of LMCache with Momento Accelerator demonstrates a significant reduction in cold start time to first token (TTFT) by over 50% by offloading key-value (KV) caches to remote storage solutions like Valkey and S3. Momento, known for its hyperscale caching and routing capabilities, supports notable companies such as Snap and Coinbase, and has developed the Momento Accelerator for AI (MAX AI), which is compatible with common frameworks like vLLM and sglang. The approach leverages a distributed KV cache offloading system that utilizes a high-bandwidth transfer engine to connect peer inference workers and storage nodes, extending GPU memory to access multi-tiered local and remote storage without discarding previous computations. Additionally, Momento Accelerator for S3 serves as a low-latency object store, providing high throughput and low latency for various applications such as live-streaming and IoT analytics. The integration of LMCache and Momento Accelerator, tested with vLLM, showcases enhanced cluster management, enabling rapid node warm-ups from durable storage and presenting numerous opportunities for further optimization in Rust.

The blog post explores the significant performance improvements achieved in large-scale AI inference clusters by utilizing distributed key-value (KV) caching in conjunction with technologies like LMCache and Momento Accelerator. By offloading the KV cache to remote storage solutions such as Valkey and S3, the system can optimize GPU performance by reducing the need for re-computation and avoiding cache eviction. Momento specializes in hyperscale caching and routing, and its Accelerator for AI (MAX AI) integrates with frameworks like vllm and sglang to enhance efficiency. The blog highlights the reduction of cold start time-to-first-token (TTFT) by over 50% through the use of persistent distributed caching, which allows for rapid warm-up of new nodes from cost-effective, durable storage, thereby supporting proactive cluster management. The upcoming focus will be on further refining LMCache and vLLM components in Rust to enable enhanced router and control plane integrations, aiming for more efficient cluster management through cache prefetching and load-aware scheduling.

The inaugural Unlocked Conference, held on January 22, 2026, at Guildhouse in San Jose, California, provided a valuable forum for engineers to share insights and experiences in performance engineering, particularly focusing on high-performance computing and system behavior. The event emphasized the importance of understanding changes in traffic patterns, data sizes, and dependencies, and how these factors affect system performance. Attendees, which included engineers from companies like Apple, Uber, and Snap, engaged in discussions about real-world scenarios, tail latency, and interaction effects between systems. Valkey, an open-source project, was highlighted for its role in improving caching and system performance, inspiring confidence among infrastructure leaders to consider migrating to it. The conference, characterized by technical talks and hallway conversations, aimed to foster a community of engineers eager to learn from each other's experiences, and future events are planned to continue this exchange of knowledge.