--- title: "Performance" description: "817 posts about Performance, summarised, each linking to the original." --- # Performance > 817 posts about Performance, summarised, each linking to the original. ## Articles ### [Production-Ready Agents Need A Production-Ready Data Platform](https://yomu.fyi/post/production-ready-agents-need-a-production-ready-data-platform.md) - Company: [MongoDB](https://yomu.fyi/company/mongodb.md) - Author: Pablo Stern - Published: Jun 11, 2026 - Type: Explainer AI development teams face constant shifts in model providers and agent frameworks, demanding data platforms that provide scalable, real-time context management. Agentic workloads require blending unstructured enterprise data, short-term session state, and persistent long-term memory. MongoDB addresses these requirements through its native JSON document model and integrated retrieval capabilities, combining full-text search, vector search, and hybrid search directly over operational data. Customer implementations such as DevRev, ElevenLabs, and Adobe rely on Atlas to achieve sub-100 millisecond hybrid retrieval and handle billions of requests. Additionally, MongoDB is collaborating with LangChain and ecosystem partners to establish open reference architectures and shared interfaces for portable agent memory across frameworks. ### [Introducing Postgres to Postgres ClickPipes in ClickHouse Cloud](https://yomu.fyi/post/introducing-postgres-to-postgres-clickpipes-in-clickhouse-cloud.md) - Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md) - Published: Jun 11, 2026 - Type: Announcement Postgres-to-Postgres migrations between providers can require manual schema handling, data movement, CDC, and cutover coordination, with risks around database objects and long-running loads. Postgres to Postgres ClickPipes, now in public beta, provides a fully managed migration service in ClickHouse Cloud designed to reduce operational overhead and support minimal downtime. It automates schema dump and restore, including secure connections, role conflicts, version differences, transactional application, retries, and UI or notification-based error reporting, while PeerDB supplies parallel initial loads and CDC capabilities. Independent pull and push processes use retries and alerting for transient errors, and the roadmap includes features to assist with cutover into Postgres by ClickHouse. ### [Making FlashAttention-4 faster for inference](https://yomu.fyi/post/making-flashattention-4-faster-for-inference.md) - Company: [Modal](https://yomu.fyi/company/modal.md) - Author: Charles Frye - Published: Jun 11, 2026 - Type: Explainer FlashAttention-4, originally designed for pre-training, required modifications to efficiently handle LLM inference workloads dominated by the memory bandwidth-limited decode phase. Modal’s contributions include adjusting parallelism strategies from query parallelism to key/value parallelism, implementing split KV to parallelize across KV tiles, and adding a heuristic for optimal splits. They added support for FP8 inputs, arbitrary KV page sizes via cp.async loads, and reduced wasted work for small query sizes by using a single query tile. The kernel also gained extended GQA packing for irregular head ratios, using cp.async for query tiles to bypass TMA alignment restrictions. These changes, measured in throughput improvements up to 3x for single-token decode and up to 2.4x for small page sizes, demonstrate the leverage of flexible parallelism and tile-based programming for inference optimization. ### [Profiling in PyTorch (Part 2): From nn.Linear to a Fused MLP](https://yomu.fyi/post/profiling-in-pytorch-part-2-from-nn-linear-to-a-fused-mlp.md) - Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md) - Author: Aritra Roy Gosthipaty, Rémi Ouazan Reboul, Sergio Paniego, Pedro Cuenca, Sayak Paul - Published: Jun 11, 2026 - Type: Tutorial Part 2 of the series examines how PyTorch profiler traces change from a single bias-enabled nn.Linear to a three-layer MLP with an intervening activation, using scripts run on an NVIDIA A100-SXM4-80GB GPU. It explains that nn.Linear represents y = x @ w.T + b, dispatches aten::addmm, and folds bias addition into the cuBLAS GEMM epilogue, while aten::t only rewrites weight strides and launches no GPU kernel. For the MLP, eager execution uses three GEMMs plus separate activation and multiplication kernels, whereas torch.compile fuses the latter operations and keeps the intermediate out of HBM, while leaving the GEMMs unchanged. A hand-written Liger Triton kernel provides the same fusion without Dynamo guards or compile latency, trading Inductor’s shape-specialized speed for generic execution across changing shapes. ### [Building ClickCannon - a tool for benchmarking ClickHouse](https://yomu.fyi/post/building-clickcannon-a-tool-for-benchmarking-clickhouse.md) - Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md) - Published: Jun 11, 2026 - Type: Benchmark ClickCannon was created to answer how much CPU, memory, and disk are needed for ClickStack observability workloads, while accounting for schema, ingestion rate, hardware, and concurrent queries. It replays about 2 TiB of real OpenTelemetry trace data, converted to ClickHouse Native format with ZSTD compression, rather than relying only on synthetic datasets. A custom multithreaded Go reader/inserter provides control over uncompressed throughput and exposes stream metrics such as compressed and uncompressed bytes, row counts, and block counts, while the framework simulates users and query workloads during ingestion. The project evolved into an open-source, generic ClickHouse benchmarking tool for custom insert and query workloads, with fine-grained control over throughput, concurrency, and user behavior; the post reports that it can push substantial throughput from a single machine. ### [Datadog and ClickHouse partner to bring full-fidelity data to modern observability](https://yomu.fyi/post/datadog-and-clickhouse-partner-to-bring-full-fidelity-data-to-modern-o.md) - Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md) - Published: Jun 10, 2026 - Type: Announcement ClickHouse and Datadog announce a partnership aimed at reducing the tradeoff between retaining complete telemetry and keeping it accessible through familiar observability workflows. New Preview capabilities let organizations route logs directly to ClickHouse through Datadog Observability Pipelines and search those logs from the Datadog Log Explorer. Pipelines can parse, enrich, filter, transform, and redact data before storage, while ClickHouse Cloud’s separation of storage and compute supports long-term retention, independently scaled query resources, and workload isolation. Federated log search queries data directly in ClickHouse, avoiding duplicate copies or re-ingestion while allowing high-volume logs to remain searchable and historical investigations to use full-fidelity telemetry. The partnership combines Datadog’s collection and investigation experience with ClickHouse’s storage and analytics foundation; the native integration and federated search are available in Preview. ### [Agents can now provision ClickHouse and Postgres on ClickHouse Cloud](https://yomu.fyi/post/agents-can-now-provision-clickhouse-and-postgres-on-clickhouse-cloud.md) - Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md) - Published: Jun 10, 2026 - Type: Announcement ClickHouse Cloud is now available through Stripe Projects, a Stripe CLI workflow that lets developers and AI agents provision real infrastructure from the command line. Running stripe projects init my-app and adding a service provisions ClickHouse or managed Postgres, returns working credentials to the environment through .env, and hands control to clickhousectl; clickhousectl greater than v0.3.0 is required. The integration supports ClickHouse for real-time analytics and managed Postgres, identified as a public beta, for transactional workloads; both services use the same CLI workflow, land in the same account, and share credentials, while Postgres uses local NVMe storage and is described as offering microsecond latency and up to 10x faster performance on I/O-heavy workloads. The feature is in developer preview, and agents can continue through clickhousectl for data ingestion with ClickPipes and service queries without manual credential handling or context switching. ### [Graviton5’s improved design increases speed and energy efficiency — beyond Moore’s law](https://yomu.fyi/post/graviton5-8217-s-improved-design-increases-speed-and-energy-efficiency.md) - Company: amazon.science - Author: Ali Saidi - Published: Jun 10, 2026 - Type: Announcement Amazon announced the general availability of M9g and M9gd EC2 instances powered by the new Graviton5 processor. Built on a three-nanometer process, Graviton5 features 192 Neoverse V3 cores, DDR5-8800 memory support, PCIe gen6 interconnects, and 192 megabytes of level-three cache. The chip transitions from seven discrete dies to four unified chiplets connected at 420 gigabytes per second, eliminating dedicated I/O and memory controller dies. These architecture updates deliver up to 25% higher computational performance over Graviton4, with up to 35% faster performance for web applications and machine learning inference. The new instances also introduce the Nitro Isolation Engine, which uses formal verification to enforce hardware and software isolation between virtual machines. ### [Bugbot is now over 3x faster, 22% cheaper, and finds 10% more bugs](https://yomu.fyi/post/bugbot-is-now-over-3x-faster-22-cheaper-and-finds-10-more-bugs.md) - Company: [Cursor](https://yomu.fyi/company/cursor.md) - Author: Jason Smale, Yuri Volkov & Michael Zhao - Published: Jun 10, 2026 - Type: Announcement Bugbot has been updated to run over three times faster, cost 22% less, and find 10% more bugs per review; 90% of runs now finish in under three minutes. The /review command lets users run Bugbot or Security Review before pushing code, while /review-bugbot and /review-security invoke those agents directly. Reviews run through /review sync with Bugbot on GitHub and GitLab, allowing a matching pull-request diff to skip a duplicate review, and Bugbot can now be configured to inspect only changes since its last review. The gains come from harness improvements and training Composer 2.5, which powers Bugbot; organizations opting out of that model use the next best available model, with speed and performance varying by configuration. ### [ClickHouse Agents: Claude-powered agentic analytics, now in public beta](https://yomu.fyi/post/clickhouse-agents-claude-powered-agentic-analytics-now-in-public-beta.md) - Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md) - Published: Jun 9, 2026 - Type: Announcement ClickHouse announced the public beta of ClickHouse Agents, a fully managed agentic analytics service in ClickHouse Cloud powered by Claude. It provides a no-code builder for agents grounded in live ClickHouse data, plus a managed chat experience in the Cloud console, so users need no SQL or separate hosting setup. Built on LibreChat, the service supports natural-language questions, SQL and visualizations, sandboxed Bash, Python, and JavaScript execution, shareable artifacts, skills, memories, MCP-compatible connections, and multi-agent workflows. Claude Sonnet and Haiku are pre-configured, while enterprise controls include SSO, encrypted message storage, and governed data access. ClickHouse says its internal deployment handles roughly 70% of data warehouse queries and processes more than 45 million tokens daily, confirming its view that real-time analytics is a natural context for high-volume agent workloads. ### [How QRT powers real-time research and risk management at petabyte scale](https://yomu.fyi/post/how-qrt-powers-real-time-research-and-risk-management-at-petabyte-scal.md) - Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md) - Published: Jun 9, 2026 - Type: Explainer Qube Research & Technologies (QRT), a global quantitative investment manager headquartered in London, uses ClickHouse Cloud for a data platform that serves researchers and for near-real-time risk monitoring, management, and P&L. Founded in 2018 as a cloud-native firm after the spinout of Credit Suisse’s proprietary trading arm, QRT chose a cloud-based approach rather than inheriting decades of on-premise infrastructure. The platform supports two major systems, while ClickStack consolidates logs, metrics, and traces for observability; alerts, log queries, and metric aggregations run against a single ClickHouse instance using standard SQL. QRT says its previous database was constrained by writer and reader counts and total cluster load, whereas ClickHouse provides greater speed and scalability, and that a unified monitoring backend simplifies maintenance while remaining reliably fast. ### [Migrating Your GitHub CI to Hugging Face Jobs](https://yomu.fyi/post/migrating-your-github-ci-to-hugging-face-jobs.md) - Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md) - Author: Abubakar Abid - Published: Jun 9, 2026 - Type: Tutorial Trackio's GitHub Actions CI was limited by generic hosted machines, maintenance-related delays, and lack of readily available GPU runners, while the project needed CPU checks and CUDA-based tests. The setup keeps GitHub Actions as the CI coordinator and uses a dispatcher Space plus the huggingface/jobs-actions bridge to launch ephemeral GitHub runners inside Hugging Face Jobs, selected through labels such as hf-jobs-cpu-upgrade and hf-jobs-t4-small. The guide covers duplicating and configuring the dispatcher, creating a GitHub App, supplying runner credentials and an HF token, changing runs-on, choosing Docker images, and validating runs and logs; Playwright and NVIDIA CUDA images are used for CPU and GPU examples. In Trackio, the CPU job took 1m10s versus a 1m40s GitHub ubuntu-latest baseline, while the GPU check passed in 45s and cost less than a cent at the t4-small rate; logs were available through both systems. ### [Managing Elasticsearch Reindex at Scale: Performance, Reliability, and Observability](https://yomu.fyi/post/managing-elasticsearch-reindex-at-scale-performance-reliability-and-ob.md) - Company: [Palantir](https://yomu.fyi/company/palantir.md) - Author: Palantir - Published: Jun 8, 2026 - Type: Explainer Palantir’s document store needs to repair or rebuild Elasticsearch indices when mappings, settings, cluster versions, or index state change, while continuing to serve live search and writes. Its reindex machinery reconstructs schema-aware indices from the strongly consistent database, rather than relying on Elasticsearch’s \_reindex API, and is designed to limit manual work and resource consumption across many deployments and schemas. For an online reindex, it creates a physical shadow index with the intended mappings and settings, backfills it from the database, dual-writes current API changes to both indices, and promotes the shadow index after completion; searches remain on the primary during the rebuild. Cross-cluster migrations use a separate machine with cluster-specific metadata, configuration, index and alias evolution, and preservation of old-cluster indices to support rollback. ### [Monitor SLAs and scale ClickHouse Cloud with clickhousectl and agents](https://yomu.fyi/post/monitor-slas-and-scale-clickhouse-cloud-with-clickhousectl-and-agents.md) - Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md) - Published: Jun 5, 2026 - Type: Tutorial ClickHouse Cloud can automatically scale infrastructure up and down, but teams may need to monitor latency SLAs for specific workloads such as a frontend dashboard requiring p99 under 200 ms. The guide tags queries with log\_comment, reads system.query\_log through clickhousectl, and aggregates query\_duration\_ms over a defined window to measure the SLA. When it is breached, operators compare query volume and latency with Prometheus-compatible CPU, memory, and concurrency metrics to distinguish increased concurrency from resource contention, then choose horizontal replica scaling or vertical replica sizing. A cron-based script can scale out when p99 exceeds a threshold, while an agent can inspect evidence and apply one scaling action using clickhousectl; actions are recorded in the ClickHouse Cloud activity log. ### [Nemotron 3.5 Content Safety: Customizable Multimodal Safety for Global Enterprise AI](https://yomu.fyi/post/nemotron-3-5-content-safety-customizable-multimodal-safety-for-global.md) - Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md) - Author: Varun Singh, Isabel Hulseman, Anuj Doshi, Shyamala Prayaga - Published: Jun 4, 2026 - Type: Announcement Nemotron 3.5 Content Safety is NVIDIA’s 4B-parameter model for evaluating user prompts, optional images, and optional assistant responses together, with multilingual coverage and enterprise-specific policy enforcement. It extends Nemotron 3 by accepting natural-language custom policies, producing optional auditable reasoning traces, and supporting binary verdicts, category-labelled verdicts, or THINK mode; disabling THINK preserves the default low-latency path. Built on Google Gemma 3 4B IT with a LoRA adapter and a 128K context window, it uses the Aegis 2.0 taxonomy of 13 core categories and 10 subcategories, and targets deployment on GPUs with 8GB or more of VRAM. The accompanying multimodal, multilingual dataset includes concise reasoning traces, while reported results average 96.5% on Multilingual Aegis, 88.8% on RTP-LX, and about 85% across the evaluated benchmark set. ### [What to look for when selecting a real-time analytical database](https://yomu.fyi/post/what-to-look-for-when-selecting-a-real-time-analytical-database.md) - Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md) - Published: Jun 4, 2026 - Type: Explainer Real-time analytical databases must serve low-latency queries over data that is continuously inserted at high volume, rather than merely query a static dataset quickly. The article frames time-to-insight as the combination of time to ingest, time to transform and prepare data, and time to query it. It argues that batch-oriented ingestion, delayed visibility, scheduled materialized-view refreshes, and cache invalidation during writes can make production latency far worse than headline benchmarks. It recommends evaluating freshness under concurrent ingest and queries, whether one engine supports structured analytics, text search, vector similarity, and JSON, and whether index maintenance can avoid blocking ingestion. As an example, Bullet reportedly moved to ClickHouse Cloud, reducing data latency by 99.4% to under five seconds, achieving millisecond queries, and handling 1,000× more data at comparable cost. ### [3x Faster Search: Parallel Test-Time Scaling with Instructed-Retriever-1](https://yomu.fyi/post/3x-faster-search-parallel-test-time-scaling-with-instructed-retriever.md) - Company: [Databricks](https://yomu.fyi/company/databricks.md) - Author: The Databricks AI Research Team - Published: Jun 4, 2026 - Type: Announcement Instructed-Retriever-1 is introduced as the retrieval-specialized model behind an update to Agent Bricks Knowledge Assistant, targeting search quality and latency. It parallelizes query and filter generation for broader candidate recall, then applies multi-pivot groupwise reranking to improve precision, using one model for both retrieval stages. The model was trained on synthetic enterprise-style environments and evaluated on realistic Knowledge Assistant workloads, where it scored 81.0 nDCG@10 for reranking and matched Claude Sonnet 4.5 retrieval quality on KARLBench. Serving uses a Mixture-of-Experts architecture, FP8 quantization, speculative decoding, and infrastructure tuning; the update reports more than 3x faster search, 2x faster answer generation, and TTFT around two seconds. End-to-end latency was consistently below 10 seconds in the offline evaluation setup. ### [Supabase Series F](https://yomu.fyi/post/supabase-series-f.md) - Company: [Supabase](https://yomu.fyi/company/supabase.md) - Author: paul\_copplestone - Published: Jun 4, 2026 - Type: Announcement Supabase has raised a $500M Series F at a $10B pre-money valuation, led by GIC. Existing investors participated, Stripe invested again, and Georgian and Salesforce Ventures joined as new investors; the funding will accelerate open-source and Postgres tooling, support growth, and provide employee liquidity. Supabase is also releasing Multigres v0.1 alpha, an open-source, self-hostable scalable operating system for Postgres designed to provide high availability and operational simplicity; a future release is intended to add Vitess-grade horizontal scaling. The alpha is ready to try but not production-ready, with production-readiness expected in a few months. Over the past year, database launches grew 600%, more than 60% of new databases came from AI tools, and nearly 10 million developers built on Supabase. ### [Designing the hf CLI as an agent-optimized way to work with the Hub](https://yomu.fyi/post/designing-the-hf-cli-as-an-agent-optimized-way-to-work-with-the-hub.md) - Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md) - Author: Célina Hanouti, Lucain Pouget - Published: Jun 4, 2026 - Type: Benchmark The post presents a redesign of hf, the Hugging Face Hub’s official CLI, for both human users and coding agents that download, upload, manage repositories, run Jobs, and administer other Hub resources. The CLI detects agents through environment variables, switches to compact, complete TSV output, and supports explicit human, agent, JSON, and quiet formats; agent mode was introduced in hf v1.9.0. Next-command hints, stderr separation, non-interactive failure messages, retry-safe operations, confirmations, and dry-run previews support multi-step automation without removing human-friendly terminal behavior. Benchmarks report equal-or-better success with roughly 1.3× to 1.8× fewer tokens than curl or the SDK on comparable tasks, and up to 6× fewer on complex workflows; the generated hf-cli skill reduced tool calls by about 30% without reducing token use or improving reliability. ### [Click-a-thon 2026: 24 hours, Bengaluru, and the fastest analytical database on the planet](https://yomu.fyi/post/click-a-thon-2026-24-hours-bengaluru-and-the-fastest-analytical-databa.md) - Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md) - Published: Jun 4, 2026 - Type: Announcement ClickHouse announces Click-a-thon 2026, its first in-person hackathon in India, scheduled for Bengaluru on August 1–2, 2026. The event invites working tech professionals to form teams of two to four for a 24-hour build with four tracks—Real-Time Analytics, Observability, Data Warehousing, and Agentic AI and Analytics—and a ₹10,00,000 prize pool. Its stated purpose is to bring developers together to build practical systems under tight constraints and experiment with ClickHouse alongside modern open-source tools. The post points to ClickStack for observability, Langfuse for LLM tracing and evaluation, and LibreChat for model and agent interfaces, while describing combinations such as streaming ingestion, materialized views, projections, analytical queries, and tool-using agents over data. Applications close June 25, with shortlists planned for the first week of July; the team captain applies on behalf of the team. [Newer posts](https://yomu.fyi/topic/performance/page/16.md) · [Older posts](https://yomu.fyi/topic/performance/page/18.md)