# Performance > 78 posts about Performance, summarised, each linking to the original. ## Articles ### [The Cloudflare Blog – Brought to you by EmDash](https://yomu.fyi/post/the-cloudflare-blog-brought-to-you-by-emdash.md) - Company: [Cloudflare](https://yomu.fyi/company/cloudflare.md) - Author: Kody Jackson - Published: Aug 24, 2026 Cloudflare migrated its official blog to EmDash, an Astro-focused content management system, acting as internal Customer Zero to evaluate scaling and usability. To prepare for traffic spikes reaching thousands of requests per second, engineers used k6 to execute ramp, breakpoint, and burst load tests. The resulting production architecture runs EmDash on Cloudflare Workers, pairing it with Workers Cache, an object cache backed by Workers KV, and Hyperdrive connected to PlanetScale. This layered caching design serves 99.5 percent of static files and 70 percent of requests from cache, flattening p95 latency under real-world loads reaching 850 requests per second. The migration also introduced a frontend redesign using the Kumo design system and added Model Context Protocol servers for automated agent workflows. ### [MetaRoCE: A New RDMA Transport Built for AI-Scale Ethernet](https://yomu.fyi/post/metaroce-a-new-rdma-transport-built-for-ai-scale-ethernet.md) - Company: [Meta](https://yomu.fyi/company/meta.md) - Author: By Arvind Srinivasan, Neil Spring, Omar Baldonado, Rajiv Krishnamurthy - Published: Aug 24, 2026 Scaling frontier AI training and inference across massive GPU clusters places severe demands on network fabrics, yet conventional RoCE relies on Priority Flow Control to enforce in-order packet delivery. Meta introduced MetaRoCE, a clean-sheet RDMA transport protocol engineered specifically for lossy, multiplane commodity Ethernet without requiring PFC. By shifting intelligence to endpoint NICs, the protocol sprays packets across fine-grained logical paths, writing out-of-order data directly into destination memory without reorder buffers or head-of-line blocking. Congestion is managed via per-path ECN windows paired with receiver-driven rate hints returned in acknowledgments. In validation on a 64-node AMD GPU cluster running collective operations, MetaRoCE outperformed RoCEv2, sustaining roughly 86% throughput under 1% packet loss and achieving linear scaling across multiplane fabrics. ### [MTIA 300: Meta’s First Training Chip with Built-in NICs and Communication-Offloading Engines](https://yomu.fyi/post/mtia-300-meta-s-first-training-chip-with-built-in-nics-and-communicati.md) - Company: [Meta](https://yomu.fyi/company/meta.md) - Author: By Rajiv Krishnamurthy, Wes Bland - Published: Aug 24, 2026 Training recommendation models poses distinct network bottlenecks because massive embedding tables trigger frequent collective operations that compete with computation on standard GPUs. Meta designed the MTIA 300 accelerator to overcome this by embedding two network chiplets containing twelve 800 Gbps RDMA NICs directly inside the chip package. The architecture offloads communication from its compute grid to sixteen dedicated message engines equipped with RISC-V cores and near-memory compute reduction blocks. Working alongside the co-designed HCCL library, the chip autonomously compiles and executes collective communication subgraphs without host CPU intervention. On a 150-billion-parameter production recommendation model, MTIA 300 delivered communication speeds 3.9 times faster than an equivalent GPU cluster while keeping concurrent compute degradation under 0.5 percent. ### [How We Optimized the Qwen 3.6 Model for Our Junie Agent](https://yomu.fyi/post/how-we-optimized-the-qwen-3-6-model-for-our-junie-agent.md) - Company: [Jetbrains](https://yomu.fyi/company/jetbrains.md) - Author: Stanislav Erokhin - Published: Aug 24, 2026 Deploying the Junie coding agent locally on Apple MacBook M5 hardware requires overcoming steep performance bottlenecks during model execution. Engineers optimized the entire stack using Qwen3.6-27B rather than Qwen3.8-27B, which severely degrades without token-heavy reasoning enabled. At the agent layer, Junie appends requests directly into a rolling context and caches initial prompt prefixes to maximize KV-cache reuse across tasks. For model inference, the team disabled reasoning, selected 4-bit quantization, and patched MLX-VLM to execute self-attention prefill matrix operations using faster 8-bit instructions on the M5 processor. Combining these prefill improvements with simultaneous multi-token prediction and n-gram speculative decoding delivered up to a 2x generation speedup. ### [Junie Can Now Run Entirely on Your Mac – No Credits, No Cloud](https://yomu.fyi/post/junie-can-now-run-entirely-on-your-mac-no-credits-no-cloud.md) - Company: [Jetbrains](https://yomu.fyi/company/jetbrains.md) - Author: Dmitry Savelev - Published: Aug 24, 2026 JetBrains has introduced Junie Local, a feature that runs the Junie coding agent entirely on local hardware without cloud dependencies, subscriptions, or credit meters. Users execute a single command inside Junie to download a 4-bit quantized Qwen3.6-27B model, requiring an Apple M5 Mac with 64 GB of RAM. The team prioritized prefill throughput over pure generation speed by utilizing 8-bit arithmetic instructions on the M5 Neural Accelerator, which increased prefill throughput by approximately 40%. Performance optimizations also include KV-cache reuse, speculative decoding to double generation speed, and disabling reasoning to preserve performance. In JetBrains' evaluations, the local configuration performed comparably to cloud-based Sonnet 4.5 on everyday tasks while ensuring source code, diffs, and prompts never leave the local environment. ### [Ready for Go 1.27 on Day One](https://yomu.fyi/post/ready-for-go-1-27-on-day-one.md) - Company: [Jetbrains](https://yomu.fyi/company/jetbrains.md) - Author: Artem Pronichev - Published: Aug 20, 2026 GoLand 2026.2 introduces day-one support for Go 1.27 language features, tooling improvements, and profiling capabilities. The Go 1.27 release adds generic methods, promoted field names in struct composite literals, improved function type inference, and a dedicated profile for detecting goroutine leaks. To assist with code maintenance, GoLand integrates official go fix modernizers directly into editor inspections, the Problems tool window, and optional pre-commit checks. Developers can capture, visualize, and analyze goroutine leak profiles alongside existing CPU, memory, and mutex profiles within the IDE. Additionally, updated Modern Go Code Guidelines provide AI coding agents with Go 1.27 context and API changes aligned with the version specified in go.mod. ### [A revisit of remote Spectre attacks on Cloudflare Workers](https://yomu.fyi/post/a-revisit-of-remote-spectre-attacks-on-cloudflare-workers.md) - Company: [Cloudflare](https://yomu.fyi/company/cloudflare.md) - Author: Martin Schwarzl - Published: Aug 19, 2026 Cloudflare reassessed remote Spectre attack feasibility within Cloudflare Workers production workloads to evaluate defenses against modern stabilization techniques. The experimental proof of concept bypassed Dynamic Process Isolation (DyPrIs) by keeping invocations alive using WebSocket traffic, which also diluted hardware performance counter ratios below detection thresholds. By combining branch mistraining, cache line eviction, and speculative type confusion, researchers transiently accessed out-of-bounds memory via raw 64-bit TypedArray backing store pointers. This attack demonstrated cross-tenant data leakage reaching up to 12 bit/s with over 99% accuracy in production. In response, Cloudflare mitigated the vulnerability by integrating the V8 memory sandbox, deploying Memory Protection Keys for hardware-assisted in-process isolation, and redesigning DyPrIs to monitor active long-lived executions. ### [Improving infrastructure efficiency for growing demand in the age of AI](https://yomu.fyi/post/improving-infrastructure-efficiency-for-growing-demand-in-the-age-of-a.md) - Company: [Dropbox](https://yomu.fyi/company/dropbox.md) - Author: Ilya Yakovlev - Published: Aug 18, 2026 Dropbox manages growing infrastructure demands from AI and standard workloads by maximizing the efficiency of its existing data centers rather than just building more capacity. Through a system-level approach spanning software, hardware, and physical facility design, the company dynamically manages power and balances workloads. These optimizations, combined with higher storage density and data-driven hardware lifecycle management, have significantly reduced energy use per unit of storage. ### [Total eclipse of the Internet: traffic impacts in Iceland, Spain, and Portugal](https://yomu.fyi/post/total-eclipse-of-the-internet-traffic-impacts-in-iceland-spain-and-por.md) - Company: [Cloudflare](https://yomu.fyi/company/cloudflare.md) - Author: Sabina Zejnilovic - Published: Aug 13, 2026 On August 12, a total solar eclipse crossed the North Atlantic and Europe, passing directly over Iceland, northern Spain, and Portugal. Cloudflare Radar analyzed HTTP request volumes in five-minute intervals across affected countries, comparing the data against a baseline derived from the previous three Wednesdays. Traffic reductions aligned closely with peak solar obscuration, with totality regions recording drops between 15% and 30% and specific areas shifting by up to -46.7%. Once the sun reappeared, request volumes rebounded to normal levels within minutes as users resumed device activity. The observed traffic declines were driven by aggregate human behavior rather than network failures. ### [How and Why Netflix Built a Real-Time Distributed Graph: Part 3 — Querying the graph with gRPC…](https://yomu.fyi/post/how-and-why-netflix-built-a-real-time-distributed-graph-part-3-queryin.md) - Company: [Netflix](https://yomu.fyi/company/netflix.md) - Author: Netflix Technology Blog - Published: Aug 7, 2026 Netflix designed a query execution serving layer for its Real-Time Distributed Graph to power sub-100ms responses across diverse graph traversal workloads spanning billions of nodes and edges. To prevent compounding network delays in multi-hop queries, the engine uses a breadth-first traversal model that batches entity lookups across entire frontier levels rather than tracing paths depth-first. The entire serving architecture relies on asynchronous composition across small thread pools of 16 to 24 threads, ensuring no thread blocks while waiting on remote storage or enrichment I/O. Selective caching via EVCache targets stable properties with volatility-matched TTLs, producing 70 to 80 percent cache hit rates and reducing backend storage calls by three to four times. A layered filtering hierarchy pushes depth and edge limits directly to the storage tier, eliminating bespoke code changes while isolating system resources against excessive fan-out. ### [GEM Training: How Meta Doubled the Efficiency of Its LLM-Scale Ads Foundation Model](https://yomu.fyi/post/gem-training-how-meta-doubled-the-efficiency-of-its-llm-scale-ads-foun.md) - Company: [Meta](https://yomu.fyi/company/meta.md) - Published: Aug 3, 2026 Meta's Generative Ads Recommendation Model (GEM) trains across thousands of GPUs using a hybrid architecture featuring trillions of sparse embedding parameters and billions of dense parameters. Standard training infrastructure built for large language models struggles with recommendation-specific workloads characterized by jagged user activity histories, asymmetric attention patterns, and numerical sensitivity. To overcome these bottlenecks, engineering teams introduced custom kernels like Jagged Flash Attention and Generalized Dot-Product Attention alongside MXFP8 mixed-precision execution. Pairing these kernel optimizations with topology-aware 5D parallelism doubled end-to-end training efficiency to 20–25% Model FLOPs Utilization while scaling total training FLOPs fourfold over twelve months. ### [Don’t stop early: Case-folding source code at memory speed](https://yomu.fyi/post/don-t-stop-early-case-folding-source-code-at-memory-speed.md) - Company: [Github](https://yomu.fyi/company/github.md) - Author: Alexander Neubeck - Published: Jul 31, 2026 GitHub's code search engine, Blackbird, must case-fold over 480TB of source code across 180 million repositories during indexing and query matching. To accelerate this operation on source code that is overwhelmingly ASCII, the engineering team replaced early-exit branching with an unconditional branch-free loop. The implementation tests uppercase ASCII ranges using wrapping arithmetic, modifies bits in place, and detects non-ASCII bytes with an accumulator register tested only after the loop completes. Eliminating data-dependent exits allowed LLVM to generate SIMD instructions and achieve throughput exceeding 45 GiB/s on an Apple M4 processor. The optimized implementation was released as the open-source Rust crate casefold. ### [Personalizing Airbnb search by learning from the guest journey](https://yomu.fyi/post/personalizing-airbnb-search-by-learning-from-the-guest-journey.md) - Company: [Airbnb](https://yomu.fyi/company/airbnb.md) - Author: Daochen Zha - Published: Jul 21, 2026 Airbnb replaced hundreds of hand-crafted ranking features with a Transformer-based sequence model that captures both long-term booking history and short-term browsing behavior. To keep latency low and throughput high, sequence representations are generated via daily batch jobs and combined with real-time queries using a co-trained setwise ranker. ### [In-House LLM Serving at Netflix](https://yomu.fyi/post/in-house-llm-serving-at-netflix.md) - Company: [Netflix](https://yomu.fyi/company/netflix.md) - Author: Netflix Technology Blog - Published: Jul 17, 2026 Netflix established an in-house serving platform to run large language model inference directly inside existing production environments alongside traditional machine learning models. Built upon NVIDIA Triton Inference Server and vLLM, the unified architecture handles member-scale routing, candidate generation, feature fetching, and model execution over gRPC and OpenAI-compatible HTTP endpoints. The team shifted their primary engine from TensorRT-LLM to vLLM to support custom architectures, simpler debugging, and non-trivial constraint logic. Addressing production obstacles required patching Triton's frontend for guided decoding, pinning dependent library versions, rewriting logits processors in C++ for vLLM V1, and handling state machine resets during engine preemptions. The resulting platform unifies deployment flows while preserving operational stability across evolving model schemas. ### [Modernizing the Meta Ads Service With an Open-Source Kernel Scheduler](https://yomu.fyi/post/modernizing-the-meta-ads-service-with-an-open-source-kernel-scheduler.md) - Company: [Meta](https://yomu.fyi/company/meta.md) - Published: Jul 13, 2026 Meta's ad serving fleet encountered latency regressions and technical debt after Linux kernel upgrades introduced the default EEVDF scheduler. To resolve these performance drops across billions of daily requests, the engineering team implemented sched\_ext, an open-source, BPF-based scheduling framework integrated into Linux kernel v6.12. The custom scheduling policy uses domain knowledge to soft-partition CPUs dynamically into separate pools for latency-critical request threads and background work, enhancing L3 cache locality. Packaged as a user-space binary, the scheduler allows policy iterations and rollouts via process restarts without kernel rebuilds. The deployment achieved a 28% reduction in p99 ads retrieval latency, a 1.1% increase in ranked ads, and 3.28 megawatts in fleet-wide power savings. ### [Better tools made Copilot code review worse. Here’s how we actually improved it.](https://yomu.fyi/post/better-tools-made-copilot-code-review-worse-here-s-how-we-actually-imp.md) - Company: [Github](https://yomu.fyi/company/github.md) - Author: Napalys Klicius - Published: Jul 10, 2026 When migrating GitHub Copilot code review from custom navigation tools to shared Unix-style CLI utilities—grep, glob, and view—benchmarks revealed higher review costs and fewer caught issues. Rather than an issue with the underlying tools, trace analysis showed the agent used general-purpose coding assistant instructions that triggered expansive repository browsing loops. In response, the team rewrote the tool guidance to enforce a review-specific workflow anchored to pull request diffs. The updated instructions direct the agent to narrow candidate call sites and files using batched discovery before reading minimal line ranges with view. This workflow tuning reduced average review costs by approximately 20% in production while maintaining review quality. ### [Scaling Grab's Data Lake: Our journey to Apache Iceberg adoption](https://yomu.fyi/post/scaling-grab-s-data-lake-our-journey-to-apache-iceberg-adoption.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Rahul Penti - Published: Jul 10, 2026 Grab transitioned its petabyte-scale data lake from Hive Parquet to Apache Iceberg to resolve metadata bottlenecks, small file fragmentation, and lack of native ACID support. Implementing Iceberg with Z-ordering reduced navigation query runtimes from 70 seconds to 6 seconds and cut daily S3 API costs by up to 95% on key operational datasets. To seamlessly manage concurrent table formats without forcing engineers to specify format prefixes, Grab developed and open-sourced UnifiedSparkCatalog. This custom catalog transparently detects table types via the Hive Metastore, routes queries to format-specific handlers, and retains backwards compatibility for legacy Hive operations. ### [Migrating Counter Service storage: Design choices and learnings](https://yomu.fyi/post/migrating-counter-service-storage-design-choices-and-learnings.md) - Company: [Grab](https://yomu.fyi/company/grab.md) - Author: Jia Long Loh - Published: Jul 3, 2026 Grab migrated its Counter Service storage backend from a legacy wide-column database to Aerospike to support real-time anti-fraud windowed aggregations across tens of thousands of queries per second. To decouple storage from the Rust reader service, engineers introduced a storage facade using enum dispatch, avoiding the per-query heap allocations of boxed trait objects. The reader used configuration-driven operating modes to support shadow reads and deterministic traffic splitting without requiring code updates. On the write path, the schema was redesigned to collapse multiple bucket records into a single sorted map per counter, using atomic server-side operations to increment counters and prune expired entries. This data model redesign reduced total record counts and in-memory primary index usage by over an order of magnitude while enabling a zero-downtime transition. ### [Meta’s AI Storage Blueprint at Scale](https://yomu.fyi/post/meta-s-ai-storage-blueprint-at-scale.md) - Company: [Meta](https://yomu.fyi/company/meta.md) - Author: By Sidharth Bajaj, Venkatraghavan Srinivasan - Published: Jul 1, 2026 Meta redesigned its BLOB-storage architecture on top of the foundational Tectonic block layer to eliminate GPU stalls and accelerate AI training workflows. The legacy storage system suffered from multi-layered metadata lookups, cross-region latency, and dataplane proxy bottlenecks that conflicted with the millisecond access requirements of flash-based AI clusters. To resolve these issues, the team collapsed disparate metadata into a single flat schema backed by ZippyDB for O(1) path lookups and introduced a fat client SDK capable of streaming data directly from storage servers. The updated stack also leverages spare GPU host memory as a distributed data cache alongside distributed read-plan caches to handle severe traffic spikes during checkpointing and model loading. These protocol and architectural changes deliver predictable latencies, reduce power consumption, and maintain high throughput during distributed training. ### [GenPage: Towards End-to-End Generative Homepage Construction at Netflix](https://yomu.fyi/post/genpage-towards-end-to-end-generative-homepage-construction-at-netflix.md) - Company: [Netflix](https://yomu.fyi/company/netflix.md) - Author: Netflix Technology Blog - Published: Jun 29, 2026 Netflix traditionally constructs its structured two-dimensional homepage through a complex, multi-stage recommender pipeline that separates candidate generation and ranking across rows and entities. To simplify this architecture and optimize directly for whole-page user satisfaction, Netflix developed GenPage, an end-to-end generative transformer model that autoregressively builds the entire homepage from raw tokenized context. The system relies on a domain-specific tokenizer to compress engagement history, context injection for cold start, hybrid row decoding to minimize decoding steps, and reinforcement learning post-training. In online A/B testing against the production baseline, GenPage delivered statistically significant gains in core user engagement metrics while cutting end-to-end serving latency by 20 percent. Offline evaluations further showed that enriching context prompts improved recommendation quality more effectively than increasing model capacity in the current operating regime. [Older posts](https://yomu.fyi/topic/performance/page/2.md)