May 2026 Summaries

In preparation for the FIFA World Cup 2026, a major UK-based broadcaster transitioned its live streaming origin from the deprecated AWS Elemental MediaStore to Momento, aiming to serve tens of millions of fans seamlessly. This complex migration involved not just replacing a core media storage and origin layer but also ensuring that Momento met stringent requirements for latency, reliability, and operational visibility while supporting existing infrastructure like encoders, packagers, and CDNs. To achieve this, the broadcaster and Momento conducted extensive tests, including latency benchmarks and load drills, to ensure performance parity or improvement and operational observability that aligns with current monitoring systems. The design goal was to make the transition as seamless as possible, maintaining existing HTTP semantics, transient data policies, and access control while introducing configurable object TTLs and ensuring long-term durability. With the new system in place, the broadcaster is poised to deliver a flawless viewing experience for the World Cup, reflecting a strategic move to an actively developed platform that aligns with future technological advancements.

Disaggregation of prefill and decode workloads in inference systems is becoming a crucial architectural shift, driven by the need to optimize both compute-heavy and latency-sensitive tasks. By separating these phases and using a KV cache transfer interface, systems can better manage workload-specific hardware requirements, as prefill and decode can operate on different nodes optimized for their respective needs. This separation transforms KV cache from an implementation detail into a fundamental distributed systems primitive, requiring new considerations for transfer latency, cache placement, and lifecycle management. Companies like NVIDIA and AWS are leading this transition by integrating disaggregated inference into their infrastructure, highlighting the economic and performance benefits of using hardware tailored to specific workload demands. As KV cache management becomes more prominent, techniques that reduce cache size enhance the practicality of disaggregation, reinforcing its importance in modern inference architectures.

Valkey-lab is a high-performance benchmarking tool designed to provide a more nuanced understanding of cache performance than traditional tools like valkey-benchmark, focusing on real-world workload conditions and latency behaviors. Unlike conventional benchmarks that offer a single throughput number, valkey-lab utilizes io_uring for efficient I/O, multi-threaded workers, and streams detailed latency data per second, enabling users to identify performance issues such as hot keys and tail spikes. By automating the saturation search process to determine the headroom number, it helps users find the maximum request rate a cache can handle before latency exceeds the Service Level Objective (SLO). Additionally, valkey-lab's output can be saved in Parquet format for detailed analysis and comparison, supporting regression testing and root cause analysis. Built on cachecannon, it requires a Linux environment with kernel 6.0+ and is built with Rust, offering insights vital for capacity planning and ensuring system reliability under production conditions.

Large payloads in cache systems like Valkey don't necessarily cause failures due to a single oversized item but rather from numerous medium-sized requests that arrive simultaneously and overwhelm the system's event loop. Despite configurations to limit payload sizes, issues such as cumulative payload volume, command shape, and write-path allocation pressure can degrade performance. Valkey 9.0's updates, such as copy avoidance, help alleviate some of these issues by reducing event-loop pressure, yet challenges persist, particularly in scenarios involving complex command shapes like MGETs, which can create inefficiencies in distributed cluster environments. Observability and runtime guardrails are crucial for managing these challenges, as demonstrated by Snap's migration from KeyDB to Valkey, where they implemented CPU throttling and custom handling at the proxy layer to mitigate large-payload problems without altering application code. This approach highlights the importance of embedding guardrails above the engine to manage system pressures dynamically, ensuring operational stability in large-scale deployments.

Snap's experience with forking KeyDB from Redis illustrates both the potential benefits and the eventual drawbacks of diverging from an established software project. Initially, Snap forked KeyDB to address Redis's limitations in handling multithreaded operations, allowing them to optimize their caching infrastructure. This decision stemmed from a need to push the capabilities of a single node further than Redis's architecture allowed. While the fork provided immediate advantages such as multithreaded command execution and zone-aware read routing, Snap eventually faced challenges in maintaining compatibility and keeping up with Redis's updates. The subsequent change in Redis's licensing and the emergence of Valkey, a new project with open governance, prompted Snap to reconsider its strategy. Valkey offered features Snap had previously developed independently, along with a collaborative development environment under the Linux Foundation. This shift, combined with internal changes and the departure of KeyDB's original creator, led Snap to migrate back to Valkey, emphasizing the importance of assessing the long-term costs and benefits of maintaining a software fork.

vLLM's hash chain and SGLang's radix tree offer distinct methods for improving prefix caching in data processing systems. vLLM utilizes a hash chain via Automatic Prefix Caching (APC) that leverages content hashing to detect shared prefixes without explicit tracking, using fixed-size blocks that facilitate efficient lookups and LRU eviction. However, this method is limited to prefix-bound content and does not support shared suffixes or segments beyond prefix positions. In contrast, SGLang employs a radix tree structure for KV cache entries, allowing prefix matching at any token boundary, which is beneficial in multi-turn conversations with variable-length shared contexts. While vLLM's approach is optimized for templated workloads and provides a flat, mmap-friendly memory layout ideal for shared or persistent cache stores, SGLang excels in scenarios with multi-turn conversations, offering higher effective hit rates and cache-aware request routing. Both systems converge effectively for agentic workloads with stable prefixes, though their architectural differences become influential in high-concurrency, variable-length scenarios.

The blog post discusses the challenges and solutions in the networking stack for disaggregated large language model (LLM) inference, highlighting the need to efficiently transfer large data volumes between GPUs. Traditional methods like PyTorch serialization and NCCL are inadequate for these high-speed requirements, leading to the development of specialized alternatives such as NIXL, UCCL, and Mooncake's Transfer Engine, each offering distinct advantages for memory abstraction, GPU-efficient peer-to-peer transfers, and bandwidth optimization, respectively. The post underscores that the shift from monolithic to distributed systems emphasizes the significance of sophisticated scheduling and data plane quality over mere hardware prowess, as demonstrated by AWS and Cerebras's collaboration using Elastic Fabric Adapter. It posits that the future of LLM serving infrastructure resembles a tiered cache network with integrated compute layers, where success hinges on treating inference pipelines as caching systems, reflecting operational lessons learned in distributed caching platforms.

Khawaja Shams explores the limitations of using synthetic data, specifically repetitive "hi hi hi" sequences, for benchmarking Key-Value (KV) cache offloading performance in inference tasks. The text highlights that such synthetic inputs lead to overly optimistic compression ratios and do not accurately reflect real-world scenarios, where diverse documents with complex structures, like medical and legal texts, affect compression and cache behavior differently. The benchmarks using these synthetic sequences fail to capture the true performance under realistic loads because the activation patterns, token diversity, and value distributions in real documents are significantly different from synthetic ones. To address this, the author emphasizes the importance of using realistic, reproducible corpora for benchmarking, which accurately represent the diverse conditions that KV caching systems will encounter in practical applications. This shift to realistic benchmarking is crucial for developing effective compression and caching strategies, as demonstrated by experiments using the Qwen3-8B-FP8 model and the introduction of advanced compression techniques like bit shuffling and TurboQuant.

In the continuation of exploring disaggregated inference, the focus shifts to efficiently moving the key-value (KV) cache between prefill and decode stages without stalling the decode process. The approach involves layer-wise streaming, where computed KV cache blocks start streaming to the decode node immediately, significantly reducing visible transfer latency. This is complemented by a tiered storage system for the KV cache, utilizing HBM, CPU DRAM, and SSDs to optimize access speed and reduce bottlenecks. Different strategies for managing the cache handoff—such as DistServe's pull method and Mooncake's push method—are evaluated, with shared storage offering a balanced alternative. The practical benefits are demonstrated in a production setting with Perplexity's KV Messenger, which uses RDMA for efficient synchronization and memory management, resulting in a significant increase in throughput. This orchestration turns the challenge from a compute-intensive problem into a cache optimization task, enabling systems like Moonshot AI's Kimi to handle significantly more requests, showcasing the transformative potential of these techniques in AI workloads.

The transformation of data warehousing through the decoupling of storage and compute, as pioneered by Snowflake, is now influencing the evolution of inference systems, with the KV cache emerging as a pivotal shared storage layer. This architectural shift allows prefill and decode processes, which have distinct resource requirements, to scale independently, enhancing efficiency and enabling previously impossible workloads. The journey towards this transformation is marked by three stages: local offloading, peer-to-peer sharing, and remote persistent storage, each representing a step toward treating the KV cache as a durable, first-class platform resource. However, the separation of storage and compute is constrained by bandwidth limitations, necessitating sophisticated architecture and hardware solutions to optimize throughput. As the industry progresses, trends such as increased intelligence density, productionized prefill services, and composable attention fragments are expected to further revolutionize inference systems. These advancements will enable context within inference systems to be treated as a durable asset, akin to how Snowflake transformed transient data outputs into valuable resources, ultimately redefining how context is managed and utilized.